1
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Li J, Cheng B, Chen Y, Gao G, Shi J, Zeng T. EWT: Efficient Wavelet-Transformer for single image denoising. Neural Netw 2024; 177:106378. [PMID: 38761414 DOI: 10.1016/j.neunet.2024.106378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 04/20/2024] [Accepted: 05/06/2024] [Indexed: 05/20/2024]
Abstract
Transformer-based image denoising methods have shown remarkable potential but suffer from high computational cost and large memory footprint due to their linear operations for capturing long-range dependencies. In this work, we aim to develop a more resource-efficient Transformer-based image denoising method that maintains high performance. To this end, we propose an Efficient Wavelet Transformer (EWT), which incorporates a Frequency-domain Conversion Pipeline (FCP) to reduce image resolution without losing critical features, and a Multi-level Feature Aggregation Module (MFAM) with a Dual-stream Feature Extraction Block (DFEB) to harness hierarchical features effectively. EWT achieves a faster processing speed by over 80% and reduces GPU memory usage by more than 60% compared to the original Transformer, while still delivering denoising performance on par with state-of-the-art methods. Extensive experiments show that EWT significantly improves the efficiency of Transformer-based image denoising, providing a more balanced approach between performance and resource consumption.
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Affiliation(s)
- Juncheng Li
- School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China; Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, 200444, China
| | - Bodong Cheng
- School of Computer Science and Technology, East China Normal University, Shanghai, 200444, China.
| | - Ying Chen
- Department of Cyberspace Security, Beijing Electronic Science and Technology Institute, Beijing, 100070, China
| | - Guangwei Gao
- Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, 210049, China; Key Laboratory of Artificial Intelligence, Ministry of Education, 200240, Shanghai, China
| | - Jun Shi
- School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China
| | - Tieyong Zeng
- Department of Mathematics, The Chinese University of Hong Kong, New Territories, 999077, Hong Kong, China.
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2
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Chinnaiyan AM, Alfred Sylam BW. Deep demosaicking convolution neural network and quantum wavelet transform-based image denoising. NETWORK (BRISTOL, ENGLAND) 2024:1-25. [PMID: 38989778 DOI: 10.1080/0954898x.2024.2358950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 05/17/2024] [Indexed: 07/12/2024]
Abstract
Demosaicking is a popular scientific area that is being explored by a vast number of scientists. Current digital imaging technologies capture colour images with a single monochrome sensor. In addition, the colour images were captured using a sensor coupled with a Colour Filter Array (CFA). Furthermore, the demosaicking procedure is required to obtain a full-colour image. Image denoising and image demosaicking are the two important image restoration techniques, which have increased popularity in recent years. Finding a suitable strategy for multiple image restoration is critical for researchers. Hence, a deep learning (DL) based image denoising and image demosaicking is developed in this research. Moreover, the Autoregressive Circle Wave Optimization (ACWO) based Demosaicking Convolutional Neural Network (DMCNN) is designed for image demosaicking. The Quantum Wavelet Transform (QWT) is used in the image denoising process. Similarly, Quantum Wavelet Transform (QWT) is used to analyse the abrupt changes in the input image with noise. The transformed image is then subjected to a thresholding technique, which determines an appropriate threshold range. Once the threshold range has been determined, soft thresholding is applied to the resulting wavelet coefficients. After that, the extraction and reconstruction of the original image is carried out using the Inverse Quantum Wavelet Transform (IQWT). Finally, the fused image is created by combining the results of both processes using a weighted average. The denoised and demosaicked images are combined using the weighted average technique. Furthermore, the proposed QWT+DMCNN-ACWO model provided the ideal values of Peak signal-to-noise ratio (PSNR), Second derivative like measure of enhancement (SDME), Structural Similarity Index (SSIM), Figure of Merit (FOM) of 0.890, and computational time of 49.549 dB, 59.53 dB, 0.963, 0.890, and 0.571, respectively.
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Affiliation(s)
- Anitha Mary Chinnaiyan
- Research Scholar, Department of Computer Science, Nesamony Memorial Christian College, Marthandam Manonmaniam Sundaranar University, Tirunelveli, India
| | - Boyed Wesley Alfred Sylam
- Department of PG Computer Science, Nesamony Memorial Christian College, Marthandam Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, India
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3
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Abrar M, Hussain D, Khan IA, Ullah F, Haq MA, Aleisa MA, Alenizi A, Bhushan S, Martha S. DeepSplice: a deep learning approach for accurate prediction of alternative splicing events in the human genome. Front Genet 2024; 15:1349546. [PMID: 38974384 PMCID: PMC11224287 DOI: 10.3389/fgene.2024.1349546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 05/21/2024] [Indexed: 07/09/2024] Open
Abstract
Alternative splicing (AS) is a crucial process in genetic information processing that generates multiple mRNA molecules from a single gene, producing diverse proteins. Accurate prediction of AS events is essential for understanding various physiological aspects, including disease progression and prognosis. Machine learning (ML) techniques have been widely employed in bioinformatics to address this challenge. However, existing models have limitations in capturing AS events in the presence of mutations and achieving high prediction performance. To overcome these limitations, this research presents deep splicing code (DSC), a deep learning (DL)-based model for AS prediction. The proposed model aims to improve predictive ability by investigating state-of-the-art techniques in AS and developing a DL model specifically designed to predict AS events accurately. The performance of the DSC model is evaluated against existing techniques, revealing its potential to enhance the understanding and predictive power of DL algorithms in AS. It outperforms other models by achieving an average AUC score of 92%. The significance of this research lies in its contribution to identifying functional implications and potential therapeutic targets associated with AS, with applications in genomics, bioinformatics, and biomedical research. The findings of this study have the potential to advance the field and pave the way for more precise and reliable predictions of AS events, ultimately leading to a deeper understanding of genetic information processing and its impact on human physiology and disease.
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Affiliation(s)
- Mohammad Abrar
- Faculty of Computer Studies, Arab Open University, Muscat, Oman
| | - Didar Hussain
- Department of Computer Science, Bacha Khan University Charsadda, Charsadda, Pakistan
| | - Izaz Ahmad Khan
- Department of Computer Science, Bacha Khan University Charsadda, Charsadda, Pakistan
| | - Fasee Ullah
- Computer and Information Sciences department, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
| | - Mohd Anul Haq
- Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, Saudi Arabia
| | - Mohammed A. Aleisa
- Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, Saudi Arabia
| | - Abdullah Alenizi
- Department of Information Technology, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, Saudi Arabia
| | - Shashi Bhushan
- Computer and Information Sciences department, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
| | - Sheshikala Martha
- School of Computer Science and Artificial Intelligence, SR University, Warangal, India
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4
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Tian C, Xiao J, Zhang B, Zuo W, Zhang Y, Lin CW. A self-supervised network for image denoising and watermark removal. Neural Netw 2024; 174:106218. [PMID: 38518709 DOI: 10.1016/j.neunet.2024.106218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 10/18/2023] [Accepted: 02/27/2024] [Indexed: 03/24/2024]
Abstract
In image watermark removal, popular methods depend on given reference non-watermark images in a supervised way to remove watermarks. However, reference non-watermark images are difficult to be obtained in the real world. At the same time, they often suffer from the influence of noise when captured by digital devices. To resolve these issues, in this paper, we present a self-supervised network for image denoising and watermark removal (SSNet). SSNet uses a parallel network in a self-supervised learning way to remove noise and watermarks. Specifically, each sub-network contains two sub-blocks. The upper sub-network uses the first sub-block to remove noise, according to noise-to-noise. Then, the second sub-block in the upper sub-network is used to remove watermarks, according to the distributions of watermarks. To prevent the loss of important information, the lower sub-network is used to simultaneously learn noise and watermarks in a self-supervised learning way. Moreover, two sub-networks interact via attention to extract more complementary salient information. The proposed method does not depend on paired images to learn a blind denoising and watermark removal model, which is very meaningful for real applications. Also, it is more effective than the popular image watermark removal methods in public datasets. Codes can be found at https://github.com/hellloxiaotian/SSNet.
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Affiliation(s)
- Chunwei Tian
- PAMI Research Group, University of Macau, 999078, Macao Special Administrative Region of China
| | - Jingyu Xiao
- School of Computer Science, Central South University, Changsha, 410083, China
| | - Bob Zhang
- PAMI Research Group, University of Macau, 999078, Macao Special Administrative Region of China.
| | - Wangmeng Zuo
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Yudong Zhang
- School of Computing and Mathematics, University of Leicester, Leicester, LE1 7RH, UK
| | - Chia-Wen Lin
- Department of Electrical Engineering and the Institute of Communications Engineering, National Tsing Hua University, Hsinchu 300, Taiwan
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5
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Salwig S, Drefs J, Lücke J. Zero-shot denoising of microscopy images recorded at high-resolution limits. PLoS Comput Biol 2024; 20:e1012192. [PMID: 38857280 PMCID: PMC11230634 DOI: 10.1371/journal.pcbi.1012192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/08/2024] [Accepted: 05/24/2024] [Indexed: 06/12/2024] Open
Abstract
Conventional and electron microscopy visualize structures in the micrometer to nanometer range, and such visualizations contribute decisively to our understanding of biological processes. Due to different factors in recording processes, microscopy images are subject to noise. Especially at their respective resolution limits, a high degree of noise can negatively effect both image interpretation by experts and further automated processing. However, the deteriorating effects of strong noise can be alleviated to a large extend by image enhancement algorithms. Because of the inherent high noise, a requirement for such algorithms is their applicability directly to noisy images or, in the extreme case, to just a single noisy image without a priori noise level information (referred to as blind zero-shot setting). This work investigates blind zero-shot algorithms for microscopy image denoising. The strategies for denoising applied by the investigated approaches include: filtering methods, recent feed-forward neural networks which were amended to be trainable on noisy images, and recent probabilistic generative models. As datasets we consider transmission electron microscopy images including images of SARS-CoV-2 viruses and fluorescence microscopy images. A natural goal of denoising algorithms is to simultaneously reduce noise while preserving the original image features, e.g., the sharpness of structures. However, in practice, a tradeoff between both aspects often has to be found. Our performance evaluations, therefore, focus not only on noise removal but set noise removal in relation to a metric which is instructive about sharpness. For all considered approaches, we numerically investigate their performance, report their denoising/sharpness tradeoff on different images, and discuss future developments. We observe that, depending on the data, the different algorithms can provide significant advantages or disadvantages in terms of their noise removal vs. sharpness preservation capabilities, which may be very relevant for different virological applications, e.g., virological analysis or image segmentation.
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Affiliation(s)
- Sebastian Salwig
- Machine Learning Lab, Department of Medical Physics and Acoustics, School of Medicine and Health Sciences, University of Oldenburg, Oldenburg, Germany
| | - Jakob Drefs
- Machine Learning Lab, Department of Medical Physics and Acoustics, School of Medicine and Health Sciences, University of Oldenburg, Oldenburg, Germany
| | - Jörg Lücke
- Machine Learning Lab, Department of Medical Physics and Acoustics, School of Medicine and Health Sciences, University of Oldenburg, Oldenburg, Germany
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6
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Nazir N, Sarwar A, Saini BS. Recent developments in denoising medical images using deep learning: An overview of models, techniques, and challenges. Micron 2024; 180:103615. [PMID: 38471391 DOI: 10.1016/j.micron.2024.103615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 02/20/2024] [Accepted: 02/26/2024] [Indexed: 03/14/2024]
Abstract
Medical imaging plays a critical role in diagnosing and treating various medical conditions. However, interpreting medical images can be challenging even for expert clinicians, as they are often degraded by noise and artifacts that can hinder the accurate identification and analysis of diseases, leading to severe consequences such as patient misdiagnosis or mortality. Various types of noise, including Gaussian, Rician, and Salt-pepper noise, can corrupt the area of interest, limiting the precision and accuracy of algorithms. Denoising algorithms have shown the potential in improving the quality of medical images by removing noise and other artifacts that obscure essential information. Deep learning has emerged as a powerful tool for image analysis and has demonstrated promising results in denoising different medical images such as MRIs, CT scans, PET scans, etc. This review paper provides a comprehensive overview of state-of-the-art deep learning algorithms used for denoising medical images. A total of 120 relevant papers were reviewed, and after screening with specific inclusion and exclusion criteria, 104 papers were selected for analysis. This study aims to provide a thorough understanding for researchers in the field of intelligent denoising by presenting an extensive survey of current techniques and highlighting significant challenges that remain to be addressed. The findings of this review are expected to contribute to the development of intelligent models that enable timely and accurate diagnoses of medical disorders. It was found that 40% of the researchers used models based on Deep convolutional neural networks to denoise the images, followed by encoder-decoder (18%) and other artificial intelligence-based techniques (15%) (Like DIP, etc.). Generative adversarial network was used by 12%, transformer-based approaches (13%) and multilayer perceptron was used by 2% of the researchers. Moreover, Gaussian noise was present in 35% of the images, followed by speckle noise (16%), poisson noise (14%), artifacts (10%), rician noise (7%), Salt-pepper noise (6%), Impulse noise (3%) and other types of noise (9%). While the progress in developing novel models for the denoising of medical images is evident, significant work remains to be done in creating standardized denoising models that perform well across a wide spectrum of medical images. Overall, this review highlights the importance of denoising medical images and provides a comprehensive understanding of the current state-of-the-art deep learning algorithms in this field.
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7
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Cui Y, Knoll A. Dual-domain strip attention for image restoration. Neural Netw 2024; 171:429-439. [PMID: 38142482 DOI: 10.1016/j.neunet.2023.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 10/28/2023] [Accepted: 12/01/2023] [Indexed: 12/26/2023]
Abstract
Image restoration aims to reconstruct a latent high-quality image from a degraded observation. Recently, the usage of Transformer has significantly advanced the state-of-the-art performance of various image restoration tasks due to its powerful ability to model long-range dependencies. However, the quadratic complexity of self-attention hinders practical applications. Moreover, sufficiently leveraging the huge spectral disparity between clean and degraded image pairs can also be conducive to image restoration. In this paper, we develop a dual-domain strip attention mechanism for image restoration by enhancing representation learning, which consists of spatial and frequency strip attention units. Specifically, the spatial strip attention unit harvests the contextual information for each pixel from its adjacent locations in the same row or column under the guidance of the learned weights via a simple convolutional branch. In addition, the frequency strip attention unit refines features in the spectral domain via frequency separation and modulation, which is implemented by simple pooling techniques. Furthermore, we apply different strip sizes for enhancing multi-scale learning, which is beneficial for handling degradations of various sizes. By employing the dual-domain attention units in different directions, each pixel can implicitly perceive information from an expanded region. Taken together, the proposed dual-domain strip attention network (DSANet) achieves state-of-the-art performance on 12 different datasets for four image restoration tasks, including image dehazing, image desnowing, image denoising, and image defocus deblurring. The code and models are available at https://github.com/c-yn/DSANet.
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Affiliation(s)
- Yuning Cui
- School of Computation, Information and Technology, Technical University of Munich, Munich, 85748, Germany.
| | - Alois Knoll
- School of Computation, Information and Technology, Technical University of Munich, Munich, 85748, Germany
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8
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Zhuge R, Wang J, Xu Z, Xu Y. Single image denoising with a feature-enhanced network. Neural Netw 2023; 168:313-325. [PMID: 37776616 DOI: 10.1016/j.neunet.2023.08.056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 07/04/2023] [Accepted: 08/30/2023] [Indexed: 10/02/2023]
Abstract
Recent Transformer-based networks have shown impressive performance on single image denoising tasks. While the Transformer model promotes the interaction of long-range features, it generally involves high computational complexity. In this paper, we propose a feature-enhanced denoising network (FEDNet) by combining CNN architectures with Transformers. Specifically, we propose an effective cross-channel attention to boost the interaction of channel information and enhance channel features. In order to fully exploit image features, we incorporate Transformer blocks into minimum-scale layers of the network, which can not only capture the long-distance dependency of low-resolution features but also reduce the multiplier-accumulator operations (MACs). Meanwhile, a structure-preserving block is designed to enhance the structural feature extraction. Experimental results on both synthetic and real-world datasets demonstrate that our model can achieve the state-of-the-art denoising performance with low computational costs.
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Affiliation(s)
- Ruibin Zhuge
- Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China; Shenzhen Key Laboratory of Visual Object Detection and Recognition, Shenzhen, 518055, China
| | - Jinghua Wang
- Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China
| | - Zenglin Xu
- Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China; Peng Cheng Laboratory, Shenzhen, 518055, China
| | - Yong Xu
- Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China; Shenzhen Key Laboratory of Visual Object Detection and Recognition, Shenzhen, 518055, China; Peng Cheng Laboratory, Shenzhen, 518055, China.
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9
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Qiu C, Huang Z, Lin C, Zhang G, Ying S. A despeckling method for ultrasound images utilizing content-aware prior and attention-driven techniques. Comput Biol Med 2023; 166:107515. [PMID: 37839221 DOI: 10.1016/j.compbiomed.2023.107515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 08/25/2023] [Accepted: 09/19/2023] [Indexed: 10/17/2023]
Abstract
The despeckling of ultrasound images contributes to the enhancement of image quality and facilitates precise treatment of conditions such as tumor cancers. However, the use of existing methods for eliminating speckle noise can cause the loss of image texture features, impacting clinical judgment. Thus, maintaining clear lesion boundaries while eliminating speckle noise is a challenging task. This paper presents an innovative approach for denoising ultrasound images using a novel noise reduction network model called content-aware prior and attention-driven (CAPAD). The model employs a neural network to automatically capture the hidden prior features in ultrasound images to guide denoising and embeds the denoiser into the optimization module to simultaneously optimize parameters and noise. Moreover, this model incorporates a content-aware attention module and a loss function that preserves the structural characteristics of the image. These additions enhance the network's capacity to capture and retain valuable information. Extensive qualitative evaluation and quantitative analysis performed on a comprehensive dataset provide compelling evidence of the model's superior denoising capabilities. It excels in noise suppression while successfully preserving the underlying structures within the ultrasound images. Compared to other denoising algorithms, it demonstrates an improvement of approximately 5.88% in PSNR and approximately 3.61% in SSIM. Furthermore, using CAPAD as a preprocessing step for breast tumor segmentation in ultrasound images can greatly improve the accuracy of image segmentation. The experimental results indicate that the utilization of CAPAD leads to a notable enhancement of 10.43% in the AUPRC for breast cancer tumor segmentation.
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Affiliation(s)
- Chenghao Qiu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610000, Sichuan, China.
| | - Zifan Huang
- School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, 524088, China.
| | - Cong Lin
- School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, 524088, China.
| | - Guodao Zhang
- Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou, 310018, China.
| | - Shenpeng Ying
- Department of Radiotherapy, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, 318000, China.
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10
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Ma R, Li S, Zhang B, Fang L, Li Z. Flexible and Generalized Real Photograph Denoising Exploiting Dual Meta Attention. IEEE TRANSACTIONS ON CYBERNETICS 2023; 53:6395-6407. [PMID: 35580100 DOI: 10.1109/tcyb.2022.3170472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Supervised deep learning techniques have been widely explored in real photograph denoising and achieved noticeable performances. However, being subject to specific training data, most current image denoising algorithms can easily be restricted to certain noisy types and exhibit poor generalizability across testing sets. To address this issue, we propose a novel flexible and well-generalized approach, coined as dual meta attention network (DMANet). The DMANet is mainly composed of a cascade of the self-meta attention blocks (SMABs) and collaborative-meta attention blocks (CMABs). These two blocks have two forms of advantages. First, they simultaneously take both spatial and channel attention into account, allowing our model to better exploit more informative feature interdependencies. Second, the attention blocks are embedded with the meta-subnetwork, which is based on metalearning and supports dynamic weight generation. Such a scheme can provide a beneficial means for self and collaborative updating of the attention maps on-the-fly. Instead of directly stacking the SMABs and CMABs to form a deep network architecture, we further devise a three-stage learning framework, where different blocks are utilized for each feature extraction stage according to the individual characteristics of SMAB and CMAB. On five real datasets, we demonstrate the superiority of our approach against the state of the art. Unlike most existing image denoising algorithms, our DMANet not only possesses a good generalization capability but can also be flexibly used to cope with the unknown and complex real noises, making it highly competitive for practical applications.
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11
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Zhang S, Liu C, Zhang Y, Liu S, Wang X. Multi-Scale Feature Learning Convolutional Neural Network for Image Denoising. SENSORS (BASEL, SWITZERLAND) 2023; 23:7713. [PMID: 37765770 PMCID: PMC10537377 DOI: 10.3390/s23187713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 08/24/2023] [Accepted: 09/01/2023] [Indexed: 09/29/2023]
Abstract
Affected by the hardware conditions and environment of imaging, images generally have serious noise. The presence of noise diminishes the image quality and compromises its effectiveness in real-world applications. Therefore, in real-world applications, reducing image noise and improving image quality are essential. Although current denoising algorithms can somewhat reduce noise, the process of noise removal may result in the loss of intricate details and adversely impact the overall image quality. Hence, to enhance the effectiveness of image denoising while preserving the intricate details of the image, this article presents a multi-scale feature learning convolutional neural network denoising algorithm (MSFLNet), which consists of three feature learning (FL) modules, a reconstruction generation module (RG), and a residual connection. The three FL modules help the algorithm learn the feature information of the image and improve the efficiency of denoising. The residual connection moves the shallow information that the model has learned to the deep layer, and RG helps the algorithm in image reconstruction and creation. Finally, our research indicates that our denoising method is effective.
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Affiliation(s)
- Shuo Zhang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (S.Z.); (Y.Z.); (S.L.); (X.W.)
- University of Chinese Academy of Sciences, Beijing 100039, China
- Key Laboratory of Space-Based Dynamic & Rapid Optical Imaging Technology, Chinese Academy of Sciences, Changchun 130033, China
| | - Chunyu Liu
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (S.Z.); (Y.Z.); (S.L.); (X.W.)
- University of Chinese Academy of Sciences, Beijing 100039, China
- Key Laboratory of Space-Based Dynamic & Rapid Optical Imaging Technology, Chinese Academy of Sciences, Changchun 130033, China
| | - Yuxin Zhang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (S.Z.); (Y.Z.); (S.L.); (X.W.)
- University of Chinese Academy of Sciences, Beijing 100039, China
- Key Laboratory of Space-Based Dynamic & Rapid Optical Imaging Technology, Chinese Academy of Sciences, Changchun 130033, China
| | - Shuai Liu
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (S.Z.); (Y.Z.); (S.L.); (X.W.)
- University of Chinese Academy of Sciences, Beijing 100039, China
- Key Laboratory of Space-Based Dynamic & Rapid Optical Imaging Technology, Chinese Academy of Sciences, Changchun 130033, China
| | - Xun Wang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; (S.Z.); (Y.Z.); (S.L.); (X.W.)
- University of Chinese Academy of Sciences, Beijing 100039, China
- Key Laboratory of Space-Based Dynamic & Rapid Optical Imaging Technology, Chinese Academy of Sciences, Changchun 130033, China
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12
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Oladyshkin S, Praditia T, Kroeker I, Mohammadi F, Nowak W, Otte S. The deep arbitrary polynomial chaos neural network or how Deep Artificial Neural Networks could benefit from data-driven homogeneous chaos theory. Neural Netw 2023; 166:85-104. [PMID: 37480771 DOI: 10.1016/j.neunet.2023.06.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/26/2023] [Accepted: 06/27/2023] [Indexed: 07/24/2023]
Abstract
Artificial Intelligence and Machine learning have been widely used in various fields of mathematical computing, physical modeling, computational science, communication science, and stochastic analysis. Approaches based on Deep Artificial Neural Networks (DANN) are very popular in our days. Depending on the learning task, the exact form of DANNs is determined via their multi-layer architecture, activation functions and the so-called loss function. However, for a majority of deep learning approaches based on DANNs, the kernel structure of neural signal processing remains the same, where the node response is encoded as a linear superposition of neural activity, while the non-linearity is triggered by the activation functions. In the current paper, we suggest to analyze the neural signal processing in DANNs from the point of view of homogeneous chaos theory as known from polynomial chaos expansion (PCE). From the PCE perspective, the (linear) response on each node of a DANN could be seen as a 1st degree multi-variate polynomial of single neurons from the previous layer, i.e. linear weighted sum of monomials. From this point of view, the conventional DANN structure relies implicitly (but erroneously) on a Gaussian distribution of neural signals. Additionally, this view revels that by design DANNs do not necessarily fulfill any orthogonality or orthonormality condition for a majority of data-driven applications. Therefore, the prevailing handling of neural signals in DANNs could lead to redundant representation as any neural signal could contain some partial information from other neural signals. To tackle that challenge, we suggest to employ the data-driven generalization of PCE theory known as arbitrary polynomial chaos (aPC) to construct a corresponding multi-variate orthonormal representations on each node of a DANN. Doing so, we generalize the conventional structure of DANNs to Deep arbitrary polynomial chaos neural networks (DaPC NN). They decompose the neural signals that travel through the multi-layer structure by an adaptive construction of data-driven multi-variate orthonormal bases for each layer. Moreover, the introduced DaPC NN provides an opportunity to go beyond the linear weighted superposition of single neurons on each node. Inheriting fundamentals of PCE theory, the DaPC NN offers an additional possibility to account for high-order neural effects reflecting simultaneous interaction in multi-layer networks. Introducing the high-order weighted superposition on each node of the network mitigates the necessity to introduce non-linearity via activation functions and, hence, reduces the room for potential subjectivity in the modeling procedure. Although the current DaPC NN framework has no theoretical restrictions on the use of activation functions. The current paper also summarizes relevant properties of DaPC NNs inherited from aPC as analytical expressions for statistical quantities and sensitivity indexes on each node. We also offer an analytical form of partial derivatives that could be used in various training algorithms. Technically, DaPC NNs require similar training procedures as conventional DANNs, and all trained weights determine automatically the corresponding multi-variate data-driven orthonormal bases for all layers of DaPC NN. The paper makes use of three test cases to illustrate the performance of DaPC NN, comparing it with the performance of the conventional DANN and also with plain aPC expansion. Evidence of convergence over the training data size against validation data sets demonstrates that the DaPC NN outperforms the conventional DANN systematically. Overall, the suggested re-formulation of the kernel network structure in terms of homogeneous chaos theory is not limited to any particular architecture or any particular definition of the loss function. The DaPC NN Matlab Toolbox is available online and users are invited to adopt it for own needs.
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Affiliation(s)
- Sergey Oladyshkin
- Department of Stochastic Simulation and Safety Research for Hydrosystems, Institute for Modelling Hydraulic and Environmental Systems, Stuttgart Center for Simulation Science, University of Stuttgart, Pfaffenwaldring 5a, 70569 Stuttgart, Germany.
| | - Timothy Praditia
- Department of Stochastic Simulation and Safety Research for Hydrosystems, Institute for Modelling Hydraulic and Environmental Systems, Stuttgart Center for Simulation Science, University of Stuttgart, Pfaffenwaldring 5a, 70569 Stuttgart, Germany
| | - Ilja Kroeker
- Department of Stochastic Simulation and Safety Research for Hydrosystems, Institute for Modelling Hydraulic and Environmental Systems, Stuttgart Center for Simulation Science, University of Stuttgart, Pfaffenwaldring 5a, 70569 Stuttgart, Germany
| | - Farid Mohammadi
- Department of Hydromechanics and Modelling of Hydrosystems, Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, Pfaffenwaldring 61, 70569 Stuttgart, Germany
| | - Wolfgang Nowak
- Department of Stochastic Simulation and Safety Research for Hydrosystems, Institute for Modelling Hydraulic and Environmental Systems, Stuttgart Center for Simulation Science, University of Stuttgart, Pfaffenwaldring 5a, 70569 Stuttgart, Germany
| | - Sebastian Otte
- Neuro-Cognitive Modeling, Computer Science Department, University of Tübingen, Sand 14, 72076 Tübingen, Germany
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13
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Rendón-Castro ÁA, Mújica-Vargas D, Luna-Álvarez A, Vianney Kinani JM. Enhancing Image Quality via Robust Noise Filtering Using Redescending M-Estimators. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1176. [PMID: 37628207 PMCID: PMC10453315 DOI: 10.3390/e25081176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/20/2023] [Accepted: 08/06/2023] [Indexed: 08/27/2023]
Abstract
In the field of image processing, noise represents an unwanted component that can occur during signal acquisition, transmission, and storage. In this paper, we introduce an efficient method that incorporates redescending M-estimators within the framework of Wiener estimation. The proposed approach effectively suppresses impulsive, additive, and multiplicative noise across varied densities. Our proposed filter operates on both grayscale and color images; it uses local information obtained from the Wiener filter and robust outlier rejection based on Insha and Hampel's tripartite redescending influence functions. The effectiveness of the proposed method is verified through qualitative and quantitative results, using metrics such as PSNR, MAE, and SSIM.
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Affiliation(s)
- Ángel Arturo Rendón-Castro
- Department of Computer Science, Tecnológico Nacional de México/CENIDET, Interior Internado Palmira S/N, Palmira, Cuernavaca 62490, Mexico; (Á.A.R.-C.)
| | - Dante Mújica-Vargas
- Department of Computer Science, Tecnológico Nacional de México/CENIDET, Interior Internado Palmira S/N, Palmira, Cuernavaca 62490, Mexico; (Á.A.R.-C.)
| | - Antonio Luna-Álvarez
- Department of Computer Science, Tecnológico Nacional de México/CENIDET, Interior Internado Palmira S/N, Palmira, Cuernavaca 62490, Mexico; (Á.A.R.-C.)
| | - Jean Marie Vianney Kinani
- Unidad Profesional Interdiciplinaria de Ingeniería Campus Hidalgo, Instituto Politécnico Nacional, Pachuca 07738, Mexico
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14
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Lee DB, Yoon HG, Park SM, Choi JW, Chen G, Kwon HY, Won C. Super-resolution of magnetic systems using deep learning. Sci Rep 2023; 13:11526. [PMID: 37460591 DOI: 10.1038/s41598-023-38335-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 07/06/2023] [Indexed: 07/20/2023] Open
Abstract
We construct a deep neural network to enhance the resolution of spin structure images formed by spontaneous symmetry breaking in the magnetic systems. Through the deep neural network, an image is expanded to a super-resolution image and reduced to the original image size to be fitted with the input feed image. The network does not require ground truth images in the training process. Therefore, it can be applied when low-resolution images are provided as training datasets, while high-resolution images are not obtainable due to the intrinsic limitation of microscope techniques. To show the usefulness of the network, we train the network with two types of simulated magnetic structure images; one is from self-organized maze patterns made of chiral magnetic structures, and the other is from magnetic domains separated by walls that are topological defects of the system. The network successfully generates high-resolution images highly correlated with the exact solutions in both cases. To investigate the effectiveness and the differences between datasets, we study the network's noise tolerance and compare the networks' reliabilities. The network is applied with experimental data obtained by magneto-optical Kerr effect microscopy and spin-polarized low-energy electron microscopy.
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Affiliation(s)
- D B Lee
- Department of Physics, Kyung Hee University, Seoul, 02447, South Korea
- Department of Battery-Smart Factory, Korea University, Seoul, 02841, South Korea
| | - H G Yoon
- Department of Physics, Kyung Hee University, Seoul, 02447, South Korea
| | - S M Park
- Department of Physics, Kyung Hee University, Seoul, 02447, South Korea
| | - J W Choi
- Center for Spintronics, Korea Institute of Science and Technology, Seoul, 02792, South Korea
| | - G Chen
- National Laboratory of Solid State Microstructures and Department of Physics, Nanjing University, Nanjing, 210093, China
- Collaborative Innovation Center of Advanced Microstructures, Nanjing, 210093, China
| | - H Y Kwon
- Center for Spintronics, Korea Institute of Science and Technology, Seoul, 02792, South Korea.
| | - C Won
- Department of Physics, Kyung Hee University, Seoul, 02447, South Korea.
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15
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Zhang J, Zhu Y, Yu W, Ma J. Considering Image Information and Self-Similarity: A Compositional Denoising Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:5915. [PMID: 37447765 PMCID: PMC10347252 DOI: 10.3390/s23135915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/15/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023]
Abstract
Recently, convolutional neural networks (CNNs) have been widely used in image denoising, and their performance has been enhanced through residual learning. However, previous research mostly focused on optimizing the network architecture of CNNs, ignoring the limitations of the commonly used residual learning. This paper identifies two of its limitations, which are the neglect of image information and the lack of effective consideration of image self-similarity. To solve these limitations, this paper proposes a compositional denoising network (CDN), which contains two sub-paths, the image information path (IIP) and the noise estimation path (NEP), respectively. IIP is trained via an image-to-image method to extract image information. For NEP, it utilizes image self-similarity from the perspective of training. This similarity-based training method constrains NEP to output similar estimated noise distributions for different image patches with a specific kind of noise. Finally, image information and noise distribution information are comprehensively considered for image denoising. Experimental results indicate that CDN outperforms other CNN-based methods in both synthetic and real-world image denoising, achieving state-of-the-art performance.
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Affiliation(s)
- Jiahong Zhang
- The State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China;
| | - Yonggui Zhu
- The School of Data Science and Media Intelligence, Communication University of China, Beijing 100024, China;
| | - Wenshu Yu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
| | - Jingning Ma
- The School of Data Science and Media Intelligence, Communication University of China, Beijing 100024, China;
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16
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Dong K, Lotfipoor A. Intelligent Bearing Fault Diagnosis Based on Feature Fusion of One-Dimensional Dilated CNN and Multi-Domain Signal Processing. SENSORS (BASEL, SWITZERLAND) 2023; 23:5607. [PMID: 37420773 DOI: 10.3390/s23125607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 05/28/2023] [Accepted: 05/28/2023] [Indexed: 07/09/2023]
Abstract
Finding relevant features that can represent different types of faults under a noisy environment is the key to practical applications of intelligent fault diagnosis. However, high classification accuracy cannot be achieved with only a few simple empirical features, and advanced feature engineering and modelling necessitate extensive specialised knowledge, resulting in restricted widespread use. This paper has proposed a novel and efficient fusion method, named MD-1d-DCNN, that combines statistical features from multiple domains and adaptive features retrieved using a one-dimensional dilated convolutional neural network. Moreover, signal processing techniques are utilised to uncover statistical features and realise the general fault information. To offset the negative influence of noise in signals and achieve high accuracy of fault diagnosis in noisy settings, 1d-DCNN is adopted to extract more dispersed and intrinsic fault-associated features, while also preventing the model from overfitting. In the end, fault classification based on fusion features is accomplished by the usage of fully connected layers. Two bearing datasets containing varying amounts of noise are used to verify the effectiveness and robustness of the suggested approach. The experimental results demonstrate MD-1d-DCNN's superior anti-noise capability. When compared to other benchmark models, the proposed method performs better at all noise levels.
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Affiliation(s)
- Kaitai Dong
- Mindsphere Analytics Centre, Digital Service, Siemens Mobility, London NW1 1AD, UK
| | - Ashkan Lotfipoor
- Institute for Infrastructure and Environment, Heriot-Watt University, Edinburgh EH14 4AS, UK
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17
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Chen Z, Wang C, Zhang F, Zhang L, Grau A, Guerra E. All-in-one aerial image enhancement network for forest scenes. FRONTIERS IN PLANT SCIENCE 2023; 14:1154176. [PMID: 37056495 PMCID: PMC10086424 DOI: 10.3389/fpls.2023.1154176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 02/28/2023] [Indexed: 06/19/2023]
Abstract
Drone monitoring plays an irreplaceable and significant role in forest firefighting due to its characteristics of wide-range observation and real-time messaging. However, aerial images are often susceptible to different degradation problems before performing high-level visual tasks including but not limited to smoke detection, fire classification, and regional localization. Recently, the majority of image enhancement methods are centered around particular types of degradation, necessitating the memory unit to accommodate different models for distinct scenarios in practical applications. Furthermore, such a paradigm requires wasted computational and storage resources to determine the type of degradation, making it difficult to meet the real-time and lightweight requirements of real-world scenarios. In this paper, we propose an All-in-one Image Enhancement Network (AIENet) that can restore various degraded images in one network. Specifically, we design a new multi-scale receptive field image enhancement block, which can better reconstruct high-resolution details of target regions of different sizes. In particular, this plug-and-play module enables it to be embedded in any learning-based model. And it has better flexibility and generalization in practical applications. This paper takes three challenging image enhancement tasks encountered in drone monitoring as examples, whereby we conduct task-specific and all-in-one image enhancement experiments on a synthetic forest dataset. The results show that the proposed AIENet outperforms the state-of-the-art image enhancement algorithms quantitatively and qualitatively. Furthermore, extra experiments on high-level vision detection also show the promising performance of our method compared with some recent baselines.
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Affiliation(s)
- Zhaoqi Chen
- College of Computer and Big Data, Fuzhou University, Fuzhou, China
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, China
| | - Chuansheng Wang
- Department of Automatic Control, Polytechnic University of Catalonia, Barcelona, Spain
| | - Fuquan Zhang
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, China
- College of Computer and Control Engineering, Minjiang University, Fuzhou, China
- Digital Media Art, Key Laboratory of Sichuan Province, Sichuan Conservatory of Music, Chengdu, China
- Fuzhou Technology Innovation Center of Intelligent Manufacturing information System, Minjiang University, Fuzhou, China
- Engineering Research Center for Intangible Cultural Heritage (ICH) Digitalization and Multi-source Information Fusion (Fujian Polytechnic Normal University), Fujian Province University, Fuzhou, China
| | - Ling Zhang
- College of Computer and Control Engineering, Minjiang University, Fuzhou, China
| | - Antoni Grau
- Department of Automatic Control, Polytechnic University of Catalonia, Barcelona, Spain
| | - Edmundo Guerra
- Department of Automatic Control, Polytechnic University of Catalonia, Barcelona, Spain
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18
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Chen X, Zhao H. A Novel Fast Reconstruction Method for Single Image Super Resolution Task. Neural Process Lett 2023. [DOI: 10.1007/s11063-023-11235-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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19
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Li B, Jiang N, Han X. Denoising of BOTDR Dynamic Strain Measurement Using Convolutional Neural Networks. SENSORS (BASEL, SWITZERLAND) 2023; 23:1764. [PMID: 36850362 PMCID: PMC9964236 DOI: 10.3390/s23041764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 01/24/2023] [Accepted: 02/01/2023] [Indexed: 06/18/2023]
Abstract
The Brillouin optical time domain reflectometry (BOTDR) system measures the distributed strain and temperature information along the optic fibre by detecting the Brillouin gain spectra (BGS) and finding the Brillouin frequency shift profiles. By introducing small gain stimulated Brillouin scattering (SBS), dynamic measurement using BOTDR can be realized, but the performance is limited due to the noise of the detected information. An image denoising method using the convolutional neural network (CNN) is applied to the derived Brillouin gain spectrum images to enhance the performance of the Brillouin frequency shift detection and the strain vibration measurement of the BOTDR system. By reducing the noise of the BGS images along the length of the fibre under test with different network depths and epoch numbers, smaller frequency uncertainties are obtained, and the sine-fitting R-squared values of the detected strain vibration profiles are also higher. The Brillouin frequency uncertainty is improved by 24% and the sine-fitting R-squared value of the obtained strain vibration profile is enhanced to 0.739, with eight layers of total depth and 200 epochs.
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Affiliation(s)
- Bo Li
- Institute of Geotechnical Engineering, Southeast University, Nanjing 211189, China
- Department of Engineering, University of Cambridge, Cambridge CP2 1PZ, UK
- Department of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA
| | - Ningjun Jiang
- Institute of Geotechnical Engineering, Southeast University, Nanjing 211189, China
- Department of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA
| | - Xiaole Han
- Department of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA
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20
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Tao H, Guo W, Han R, Yang Q, Zhao J. RDASNet: Image Denoising via a Residual Dense Attention Similarity Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:1486. [PMID: 36772535 PMCID: PMC9921182 DOI: 10.3390/s23031486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 01/14/2023] [Accepted: 01/19/2023] [Indexed: 06/18/2023]
Abstract
In recent years, thanks to the performance advantages of convolutional neural networks (CNNs), CNNs have been widely used in image denoising. However, most of the CNN-based image-denoising models cannot make full use of the redundancy of image data, which limits the expressiveness of the model. We propose a new image-denoising model that aims to extract the local features of the image through CNN and focus on the global information of the image through the attention similarity module (ASM), especially the global similarity details of the image. Furthermore, dilation convolution is used to enlarge the receptive field to better focus on the global features. Moreover, avg-pooling is used to smooth and suppress noise in the ASM to further improve model performance. In addition, through global residual learning, the effect is enhanced from shallow to deep layers. A large number of experiments show that our proposed model has a better image-denoising effect, including quantitative and visual results. It is more suitable for complex blind noise and real images.
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Affiliation(s)
- Haowu Tao
- School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
| | - Wenhua Guo
- State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Rui Han
- State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Qi Yang
- School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
| | - Jiyuan Zhao
- State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
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21
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An application of deep dual convolutional neural network for enhanced medical image denoising. Med Biol Eng Comput 2023; 61:991-1004. [PMID: 36639550 DOI: 10.1007/s11517-022-02731-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 12/09/2022] [Indexed: 01/15/2023]
Abstract
This work investigates the medical image denoising (MID) application of the dual denoising network (DudeNet) model for chest X-ray (CXR). The DudeNet model comprises four components: a feature extraction block with a sparse mechanism, an enhancement block, a compression block, and a reconstruction block. The developed model uses residual learning to boost denoising performance and batch normalization to accelerate the training process. The name proposed for this model is dual convolutional medical image-enhanced denoising network (DCMIEDNet). The peak signal-to-noise ratio (PSNR) and structure similarity index measurement (SSIM) are used to assess the MID performance for five different additive white Gaussian noise (AWGN) levels of σ = 15, 25, 40, 50, and 60 in CXR images. Presented investigations revealed that the PSNR and SSIM offered by DCMIEDNet are better than several popular state-of-the-art models such as block matching and 3D filtering, denoising convolutional neural network, and feature-guided denoising convolutional neural network. In addition, it is also superior to the recently reported MID models like deep convolutional neural network with residual learning, real-valued medical image denoising network, and complex-valued medical image denoising network. Therefore, based on the presented experiments, it is concluded that applying the DudeNet methodology for DCMIEDNet promises to be quite helpful for physicians.
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22
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Isgut M, Gloster L, Choi K, Venugopalan J, Wang MD. Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era. IEEE Rev Biomed Eng 2023; 16:53-69. [PMID: 36269930 DOI: 10.1109/rbme.2022.3216531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
At the beginning of the COVID-19 pandemic, there was significant hype about the potential impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or surveillance. However, AI tools have not yet been widely successful. One of the key reason is the COVID-19 pandemic has demanded faster real-time development of AI-driven clinical and health support tools, including rapid data collection, algorithm development, validation, and deployment. However, there was not enough time for proper data quality control. Learning from the hard lessons in COVID-19, we summarize the important health data quality challenges during COVID-19 pandemic such as lack of data standardization, missing data, tabulation errors, and noise and artifact. Then we conduct a systematic investigation of computational methods that address these issues, including emerging novel advanced AI data quality control methods that achieve better data quality outcomes and, in some cases, simplify or automate the data cleaning process. We hope this article can assist healthcare community to improve health data quality going forward with novel AI development.
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Monkam P, Lu W, Jin S, Shan W, Wu J, Zhou X, Tang B, Zhao H, Zhang H, Ding X, Chen H, Su L. US-Net: A lightweight network for simultaneous speckle suppression and texture enhancement in ultrasound images. Comput Biol Med 2023; 152:106385. [PMID: 36493732 DOI: 10.1016/j.compbiomed.2022.106385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 11/21/2022] [Accepted: 11/28/2022] [Indexed: 12/02/2022]
Abstract
BACKGROUND Numerous traditional filtering approaches and deep learning-based methods have been proposed to improve the quality of ultrasound (US) image data. However, their results tend to suffer from over-smoothing and loss of texture and fine details. Moreover, they perform poorly on images with different degradation levels and mainly focus on speckle reduction, even though texture and fine detail enhancement are of crucial importance in clinical diagnosis. METHODS We propose an end-to-end framework termed US-Net for simultaneous speckle suppression and texture enhancement in US images. The architecture of US-Net is inspired by U-Net, whereby a feature refinement attention block (FRAB) is introduced to enable an effective learning of multi-level and multi-contextual representative features. Specifically, FRAB aims to emphasize high-frequency image information, which helps boost the restoration and preservation of fine-grained and textural details. Furthermore, our proposed US-Net is trained essentially with real US image data, whereby real US images embedded with simulated multi-level speckle noise are used as an auxiliary training set. RESULTS Extensive quantitative and qualitative experiments indicate that although trained with only one US image data type, our proposed US-Net is capable of restoring images acquired from different body parts and scanning settings with different degradation levels, while exhibiting favorable performance against state-of-the-art image enhancement approaches. Furthermore, utilizing our proposed US-Net as a pre-processing stage for COVID-19 diagnosis results in a gain of 3.6% in diagnostic accuracy. CONCLUSIONS The proposed framework can help improve the accuracy of ultrasound diagnosis.
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Affiliation(s)
- Patrice Monkam
- Department of Automation, Tsinghua University, Beijing, China; Beijing National Research Center for Information Science and Technology (BNRist), China.
| | - Wenkai Lu
- Department of Automation, Tsinghua University, Beijing, China; Beijing National Research Center for Information Science and Technology (BNRist), China.
| | - Songbai Jin
- Department of Automation, Tsinghua University, Beijing, China; Beijing National Research Center for Information Science and Technology (BNRist), China.
| | - Wenjun Shan
- Department of Automation, Tsinghua University, Beijing, China; Beijing National Research Center for Information Science and Technology (BNRist), China.
| | - Jing Wu
- Department of Automation, Tsinghua University, Beijing, China; Beijing National Research Center for Information Science and Technology (BNRist), China.
| | - Xiang Zhou
- Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.
| | - Bo Tang
- Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.
| | - Hua Zhao
- Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.
| | - Hongmin Zhang
- Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.
| | - Xin Ding
- Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.
| | - Huan Chen
- Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.
| | - Longxiang Su
- Department of Critical Care Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.
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Kim W, Lee J, Kang M, Kim JS, Choi JH. Wavelet subband-specific learning for low-dose computed tomography denoising. PLoS One 2022; 17:e0274308. [PMID: 36084002 PMCID: PMC9462582 DOI: 10.1371/journal.pone.0274308] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 08/25/2022] [Indexed: 11/19/2022] Open
Abstract
Deep neural networks have shown great improvements in low-dose computed tomography (CT) denoising. Early algorithms were primarily optimized to obtain an accurate image with low distortion between the denoised image and reference full-dose image at the cost of yielding an overly smoothed unrealistic CT image. Recent research has sought to preserve the fine details of denoised images with high perceptual quality, which has been accompanied by a decrease in objective quality due to a trade-off between perceptual quality and distortion. We pursue a network that can generate accurate and realistic CT images with high objective and perceptual quality within one network, achieving a better perception-distortion trade-off. To achieve this goal, we propose a stationary wavelet transform-assisted network employing the characteristics of high- and low-frequency domains of the wavelet transform and frequency subband-specific losses defined in the wavelet domain. We first introduce a stationary wavelet transform for the network training procedure. Then, we train the network using objective loss functions defined for high- and low-frequency domains to enhance the objective quality of the denoised CT image. With this network design, we train the network again after replacing the objective loss functions with perceptual loss functions in high- and low-frequency domains. As a result, we acquired denoised CT images with high perceptual quality using this strategy while minimizing the objective quality loss. We evaluated our algorithms on the phantom and clinical images, and the quantitative and qualitative results indicate that ours outperform the existing state-of-the-art algorithms in terms of objective and perceptual quality.
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Affiliation(s)
- Wonjin Kim
- Division of Mechanical and Biomedical Engineering, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, Republic of Korea
| | - Jaayeon Lee
- Division of Mechanical and Biomedical Engineering, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, Republic of Korea
| | - Mihyun Kang
- Department of Cyber Security, Ewha Womans University, Seoul, Republic of Korea
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jang-Hwan Choi
- Division of Mechanical and Biomedical Engineering, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, Republic of Korea
- * E-mail:
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25
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Enhanced statistical nearest neighbors with steerable pyramid transform for Gaussian noise removal in a color image. EVOLUTIONARY INTELLIGENCE 2022. [DOI: 10.1007/s12065-021-00627-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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26
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Wang H, Liu Z, Peng D, Cheng Z. Attention-guided joint learning CNN with noise robustness for bearing fault diagnosis and vibration signal denoising. ISA TRANSACTIONS 2022; 128:470-484. [PMID: 34961609 DOI: 10.1016/j.isatra.2021.11.028] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 11/15/2021] [Accepted: 11/15/2021] [Indexed: 06/14/2023]
Abstract
Mechanical system usually operates in harsh environments, and the monitored vibration signal faces substantial noise interference, which brings great challenges to the robust fault diagnosis. This paper proposes a novel attention-guided joint learning convolutional neural network (JL-CNN) for mechanical equipment condition monitoring. Fault diagnosis task (FD-Task) and signal denoising task (SD-Task) are integrated into an end-to-end CNN architecture, achieving good noise robustness through dual-task joint learning. JL-CNN mainly includes a joint feature encoding network and two attention-based encoder networks. This architecture allows FD-Task and SD-Task can achieve deep cooperation and mutual learning. The JL-CNN is evaluated on the wheelset bearing dataset and motor bearing dataset, which shows that JL-CNN has excellent fault diagnosis ability and signal denoising ability, and it has good performance under strong noise and unknown noise.
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Affiliation(s)
- Huan Wang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Zhiliang Liu
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Institute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan, 523808, China.
| | - Dandan Peng
- Department of Mechanical Engineering, KU Leuven, Leuven, Belgium; Dynamics of Mechanical and Mechatronic Systems, Flanders Make, Belgium
| | - Zhe Cheng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; Laboratory of Science and Technology on Integrated Logistics Support, NUDT, Changsha 410073, China
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27
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Image super-resolution with an enhanced group convolutional neural network. Neural Netw 2022; 153:373-385. [DOI: 10.1016/j.neunet.2022.06.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 05/27/2022] [Accepted: 06/06/2022] [Indexed: 11/23/2022]
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28
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Li J, Tang C, Xu M, Lei Z. Uneven wrapped phase pattern denoising using a deep neural network. APPLIED OPTICS 2022; 61:7150-7157. [PMID: 36256334 DOI: 10.1364/ao.461967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 08/04/2022] [Indexed: 06/16/2023]
Abstract
The wrapped phase patterns obtained from an object composed of different materials have uneven gray values. In this paper, we improve the dilated-blocks-based deep convolution neural network (DBDNet) and build a new dataset for restoring the uneven gray values of uneven wrapped phase patterns as well as eliminating the speckle noise. In our method, we improve the structure of dilated blocks in DBDNet to enhance the ability of obtaining full scales of gray values and speckle noise information in the uneven phase patterns. We use the combined MS_SSIM+L1 loss function to improve the denoising and restoration performance of our method. We compare three representative networks ResNet-based, ADNet, and BRDNet in denoising with our proposed method. We test the three compared methods and our method on one group of computer-simulated and one group of experimentally obtained uneven noisy wrapped phase patterns from a dynamic measurement. We also conduct the ablation experiments on the improved model structure and the combined loss function used in our method. The denoising performance has been evaluated quantitatively and qualitatively. The denoising results demonstrate that our proposed method can reduce high speckle noise, restore the uneven gray values of wrapped phase patterns, and get better results than the compared methods.
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29
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Total generalized variational-liked network for image denoising. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03717-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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30
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Neili Z, Sundaraj K. A comparative study of the spectrogram, scalogram, melspectrogram and gammatonegram time-frequency representations for the classification of lung sounds using the ICBHI database based on CNNs. BIOMED ENG-BIOMED TE 2022; 67:367-390. [PMID: 35926850 DOI: 10.1515/bmt-2022-0180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 06/21/2022] [Indexed: 11/15/2022]
Abstract
In lung sound classification using deep learning, many studies have considered the use of short-time Fourier transform (STFT) as the most commonly used 2D representation of the input data. Consequently, STFT has been widely used as an analytical tool, but other versions of the representation have also been developed. This study aims to evaluate and compare the performance of the spectrogram, scalogram, melspectrogram and gammatonegram representations, and provide comparative information to users regarding the suitability of these time-frequency (TF) techniques in lung sound classification. Lung sound signals used in this study were obtained from the ICBHI 2017 respiratory sound database. These lung sound recordings were converted into images of spectrogram, scalogram, melspectrogram and gammatonegram TF representations respectively. The four types of images were fed separately into the VGG16, ResNet-50 and AlexNet deep-learning architectures. Network performances were analyzed and compared based on accuracy, precision, recall and F1-score. The results of the analysis on the performance of the four representations using these three commonly used CNN deep-learning networks indicate that the generated gammatonegram and scalogram TF images coupled with ResNet-50 achieved maximum classification accuracies.
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Affiliation(s)
- Zakaria Neili
- Electronics Department, University of Badji Mokhtar Annaba, Annaba, Algeria
| | - Kenneth Sundaraj
- Faculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
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31
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Huang B, Xu T, Shen Z, Jiang S, Zhao B, Bian Z. SiamATL: Online Update of Siamese Tracking Network via Attentional Transfer Learning. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:7527-7540. [PMID: 33417585 DOI: 10.1109/tcyb.2020.3043520] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Visual object tracking with semantic deep features has recently attracted much attention in computer vision. Especially, Siamese trackers, which aim to learn a decision making-based similarity evaluation, are widely utilized in the tracking community. However, the online updating of the Siamese fashion is still a tricky issue due to the limitation, which is a tradeoff between model adaption and degradation. To address such an issue, in this article, we propose a novel attentional transfer learning-based Siamese network (SiamATL), which fully exploits the previous knowledge to inspire the current tracker learning in the decision-making module. First, we explicitly model the template and surroundings by using an attentional online update strategy to avoid template pollution. Then, we introduce an instance-transfer discriminative correlation filter (ITDCF) to enhance the distinguishing ability of the tracker. Finally, we suggest a mutual compensation mechanism that integrates cross-correlation matching and ITDCF detection into the decision-making subnetwork to achieve online tracking. Comprehensive experiments demonstrate that our approach outperforms state-of-the-art tracking algorithms on multiple large-scale tracking datasets.
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32
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Chen J, Zhang H, Zhang H, Ma A, Su Y, Li W. An image denoising method of picking robot vision based on feature pyramid network. J FOOD PROCESS ENG 2022. [DOI: 10.1111/jfpe.14117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Jiqing Chen
- College of Mechatronic Engineering Guangxi University Nanning People's Republic of China
- Guangxi Manufacturing System and Advanced Manufacturing Technology Key Laboratory Nanning People's Republic of China
| | - Hongdu Zhang
- College of Mechatronic Engineering Guangxi University Nanning People's Republic of China
| | - Haiyan Zhang
- College of Mechatronic Engineering Guangxi University Nanning People's Republic of China
| | - Aoqiang Ma
- College of Mechatronic Engineering Guangxi University Nanning People's Republic of China
| | - Yousheng Su
- College of Mechatronic Engineering Guangxi University Nanning People's Republic of China
| | - Wenqu Li
- College of Mechatronic Engineering Guangxi University Nanning People's Republic of China
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33
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A Multi-scale Dilated Residual Convolution Network for Image Denoising. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10934-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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34
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Ma R, Zhang B, Zhou Y, Li Z, Lei F. PID Controller-Guided Attention Neural Network Learning for Fast and Effective Real Photographs Denoising. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:3010-3023. [PMID: 33449884 DOI: 10.1109/tnnls.2020.3048031] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Real photograph denoising is extremely challenging in low-level computer vision since the noise is sophisticated and cannot be fully modeled by explicit distributions. Although deep-learning techniques have been actively explored for this issue and achieved convincing results, most of the networks may cause vanishing or exploding gradients, and usually entail more time and memory to obtain a remarkable performance. This article overcomes these challenges and presents a novel network, namely, PID controller guide attention neural network (PAN-Net), taking advantage of both the proportional-integral-derivative (PID) controller and attention neural network for real photograph denoising. First, a PID-attention network (PID-AN) is built to learn and exploit discriminative image features. Meanwhile, we devise a dynamic learning scheme by linking the neural network and control action, which significantly improves the robustness and adaptability of PID-AN. Second, we explore both the residual structure and share-source skip connections to stack the PID-ANs. Such a framework provides a flexible way to feature residual learning, enabling us to facilitate the network training and boost the denoising performance. Extensive experiments show that our PAN-Net achieves superior denoising results against the state-of-the-art in terms of image quality and efficiency.
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35
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Divya S, Padma Suresh L, John A. Enhanced deep-joint segmentation with deep learning networks of glioma tumor for multi-grade classification using MR images. Pattern Anal Appl 2022. [DOI: 10.1007/s10044-022-01064-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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36
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Teng Q, Liu Z, Song Y, Han K, Lu Y. A survey on the interpretability of deep learning in medical diagnosis. MULTIMEDIA SYSTEMS 2022; 28:2335-2355. [PMID: 35789785 PMCID: PMC9243744 DOI: 10.1007/s00530-022-00960-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 05/29/2022] [Indexed: 06/15/2023]
Abstract
Deep learning has demonstrated remarkable performance in the medical domain, with accuracy that rivals or even exceeds that of human experts. However, it has a significant problem that these models are "black-box" structures, which means they are opaque, non-intuitive, and difficult for people to understand. This creates a barrier to the application of deep learning models in clinical practice due to lack of interpretability, trust, and transparency. To overcome this problem, several studies on interpretability have been proposed. Therefore, in this paper, we comprehensively review the interpretability of deep learning in medical diagnosis based on the current literature, including some common interpretability methods used in the medical domain, various applications with interpretability for disease diagnosis, prevalent evaluation metrics, and several disease datasets. In addition, the challenges of interpretability and future research directions are also discussed here. To the best of our knowledge, this is the first time that various applications of interpretability methods for disease diagnosis have been summarized.
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Affiliation(s)
- Qiaoying Teng
- School of Computer Science, Jilin Normal University, Siping, 136000 China
| | - Zhe Liu
- School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 212013 China
| | - Yuqing Song
- School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 212013 China
| | - Kai Han
- School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 212013 China
| | - Yang Lu
- School of Computer Science, Jilin Normal University, Siping, 136000 China
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37
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Hybrid neural networks for noise reductions of integrated navigation complexes. ARTIF INTELL 2022. [DOI: 10.15407/jai2022.01.288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The necessity of integrated navigation complexes (INC) construction is substantiated. It is proposed to include in the complex the following inertial systems: inertial, satellite and visual. It helps to increase the accuracy of determining the coordinates of unmanned aerial vehicles. It is shown that in unfavorable cases, namely the suppression of external noise of the satellite navigation system, an increase in the errors of the inertial navigation system (INS), including through the use of accelerometers and gyroscopes manufactured using MEMS technology, the presence of bad weather conditions, which complicates the work of the visual navigation system. In order to ensure the operation of the navigation complex, it is necessary to ensure the suppression of interference (noise). To improve the accuracy of the INS, which is part of the INC, it is proposed to use the procedure for extracting noise from the raw signal of the INS, its prediction using neural networks and its suppression. To solve this problem, two approaches are proposed, the first of which is based on the use of a multi-row GMDH algorithm and single-layer networks with sigm_piecewise neurons, and the second is on the use of hybrid recurrent neural networks, when neural networks were used, which included long-term and short-term memory (LSTM) and Gated Recurrent Units (GRU). Various types of noise, that are inherent in video images in visual navigation systems are considered: Gaussian noise, salt and pepper noise, Poisson noise, fractional noise, blind noise. Particular attention is paid to blind noise. To improve the accuracy of the visual navigation system, it is proposed to use hybrid convolutional neural networks.
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38
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An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12126227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Image denoising is a classic but still important issue in image processing as the denoising effect has a significant impact on subsequent image processing results, such as target recognition and edge detection. In the past few decades, various denoising methods have been proposed, such as model-based and learning-based methods, and they have achieved promising results. However, no stand-alone method consistently outperforms the others in different complex imaging situations. Based on the complementary strengths of model-based and learning-based methods, in this study, we design a pixel-level image combination strategy to leverage their respective advantages for the denoised images (referred to as initial denoised images) generated by individual denoisers. The key to this combination strategy is to generate a corresponding weight map of the same size for each initial denoised image. To this end, we introduce an unsupervised weight map generative network that adjusts its parameters to generate a weight map for each initial denoised image under the guidance of our designed loss function. Using the weight maps, we are able to fully utilize the internal and external information of various denoising methods at a finer granularity, ensuring that the final combined image is close to the optimal. To the best of our knowledge, our enhancement method of combining denoised images at the pixel level is the first proposed in the image combination field. Extensive experiments demonstrate that the proposed method shows superior performance, both quantitatively and visually, and stronger generalization. Specifically, in comparison with the stand-alone denoising methods FFDNet and BM3D, our method improves the average peak signal-to-noise ratio (PSNR) by 0.18 dB to 0.83 dB on two benchmarking datasets crossing different noise levels. Its denoising effect is also greater than other competitive stand-alone methods and combination methods, and has surpassed the denoising effect of the second-best method by 0.03 dB to 1.42 dB. It should be noted that since our image combination strategy is generic, the proposed combined strategy can not only be used for image denoising but can also be extended to low-light image enhancement, image deblurring or image super-resolution.
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39
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Zhang Q, Xiao J, Tian C, Chun‐Wei Lin J, Zhang S. A robust deformed convolutional neural network (CNN) for image denoising. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2022. [DOI: 10.1049/cit2.12110] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Qi Zhang
- School of Economics and Management Harbin Institute of Technology at Weihai Weihai China
| | - Jingyu Xiao
- School of Computer Science Central South University Changsha China
| | - Chunwei Tian
- School of Software Northwestern Polytechnical University Xi'an Shaanxi China
- Research & Development Institute Northwestern Polytechnical University Shenzhen China
- Yangtze River Delta Research Institute Northwestern Polytechnical University Taicang China
| | - Jerry Chun‐Wei Lin
- Department of Computer Science, Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway
| | - Shichao Zhang
- School of Computer Science Central South University Changsha China
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40
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Fatima, Imran M, Ullah A, Arif M, Noor R. A unified technique for entropy enhancement based diabetic retinopathy detection using hybrid neural network. Comput Biol Med 2022; 145:105424. [DOI: 10.1016/j.compbiomed.2022.105424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/09/2022] [Accepted: 03/17/2022] [Indexed: 02/07/2023]
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41
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Zhang H, Lian Q, Zhao J, Wang Y, Yang Y, Feng S. RatUNet: residual U-Net based on attention mechanism for image denoising. PeerJ Comput Sci 2022; 8:e970. [PMID: 35634105 PMCID: PMC9138094 DOI: 10.7717/peerj-cs.970] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 04/11/2022] [Indexed: 06/15/2023]
Abstract
Deep convolutional neural networks (CNNs) have been very successful in image denoising. However, with the growth of the depth of plain networks, CNNs may result in performance degradation. The lack of network depth leads to the limited ability of the network to extract image features and difficults to fuse the shallow image features into the deep image information. In this work, we propose an improved deep convolutional U-Net framework (RatUNet) for image denoising. RatUNet improves Unet as follows: (1) RatUNet uses the residual blocks of ResNet to deepen the network depth, so as to avoid the network performance saturation. (2) RatUNet improves the down-sampling method, which is conducive to extracting image features. (3) RatUNet improves the up-sampling method, which is used to restore image details. (4) RatUNet improves the skip-connection method of the U-Net network, which is used to fuse the shallow feature information into the deep image details, and it is more conducive to restore the clean image. (5) In order to better process the edge information of the image, RatUNet uses depthwise and polarized self-attention mechanism to guide a CNN for image denoising. Extensive experiments show that our RatUNet is more efficient and has better performance than existing state-of-the-art denoising methods, especially in SSIM metrics, the denoising effect of the RatUNet achieves very high performance. Visualization results show that the denoised image by RatUNet is smoother and sharper than other methods.
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Affiliation(s)
- Huibin Zhang
- Institute of Information Science and Technology, Yanshan University, Qinhuang Dao, Hebei Province, China
- Computer Department, Xinzhou Teachers University, Xinzhou, Shanxi Province, China
| | - Qiusheng Lian
- Institute of Information Science and Technology, Yanshan University, Qinhuang Dao, Hebei Province, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qin Huangdao, Hebei Province, China
| | - Jianmin Zhao
- Institute of Information Science and Technology, Yanshan University, Qinhuang Dao, Hebei Province, China
- School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia Province, China
| | - Yining Wang
- Computer Department, Xinzhou Teachers University, Xinzhou, Shanxi Province, China
| | - Yuchi Yang
- Institute of Information Science and Technology, Yanshan University, Qinhuang Dao, Hebei Province, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qin Huangdao, Hebei Province, China
| | - Suqin Feng
- Computer Department, Xinzhou Teachers University, Xinzhou, Shanxi Province, China
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42
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Yang Z, Leng L, Li M, Chu J. A computer-aid multi-task light-weight network for macroscopic feces diagnosis. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:15671-15686. [PMID: 35250359 PMCID: PMC8884099 DOI: 10.1007/s11042-022-12565-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 06/15/2021] [Accepted: 01/31/2022] [Indexed: 06/14/2023]
Abstract
The abnormal traits and colors of feces typically indicate that the patients are probably suffering from tumor or digestive-system diseases. Thus a fast, accurate and automatic health diagnosis system based on feces is urgently necessary for improving the examination speed and reducing the infection risk. The rarity of the pathological images would deteriorate the accuracy performance of the trained models. In order to alleviate this problem, we employ augmentation and over-sampling to expand the samples of the classes that have few samples in the training batch. In order to achieve an impressive recognition performance and leverage the latent correlation between the traits and colors of feces pathological samples, a multi-task network is developed to recognize colors and traits of the macroscopic feces images. The parameter number of a single multi-task network is generally much smaller than the total parameter number of multiple single-task networks, so the storage cost is reduced. The loss function of the multi-task network is the weighted sum of the losses of the two tasks. In this paper, the weights of the tasks are determined according to their difficulty levels that are measured by the fitted linear functions. The sufficient experiments confirm that the proposed method can yield higher accuracies, and the efficiency is also improved.
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Affiliation(s)
- Ziyuan Yang
- School of Software, Nanchang Hangkong University, Nanchang, 330063 People’s Republic of China
- College of Computer Science, Sichuan University, Chengdu, 610065 People’s Republic of China
| | - Lu Leng
- School of Software, Nanchang Hangkong University, Nanchang, 330063 People’s Republic of China
- School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, 120749 Republic of Korea
| | - Ming Li
- School of Information Engineering, Nanchang Hangkong University, Nanchang, 330063 People’s Republic of China
| | - Jun Chu
- School of Software, Nanchang Hangkong University, Nanchang, 330063 People’s Republic of China
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43
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Zhang L, Zhang J. Ultrasound image denoising using generative adversarial networks with residual dense connectivity and weighted joint loss. PeerJ Comput Sci 2022; 8:e873. [PMID: 35494868 PMCID: PMC9044345 DOI: 10.7717/peerj-cs.873] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 01/11/2022] [Indexed: 06/12/2023]
Abstract
BACKGROUND Ultrasound imaging has been recognized as a powerful tool in clinical diagnosis. Nonetheless, the presence of speckle noise degrades the signal-to-noise of ultrasound images. Various denoising algorithms cannot fully reduce speckle noise and retain image features well for ultrasound imaging. The application of deep learning in ultrasound image denoising has attracted more and more attention in recent years. METHODS In the article, we propose a generative adversarial network with residual dense connectivity and weighted joint loss (GAN-RW) to avoid the limitations of traditional image denoising algorithms and surpass the most advanced performance of ultrasound image denoising. The denoising network is based on U-Net architecture which includes four encoder and four decoder modules. Each of the encoder and decoder modules is replaced with residual dense connectivity and BN to remove speckle noise. The discriminator network applies a series of convolutional layers to identify differences between the translated images and the desired modality. In the training processes, we introduce a joint loss function consisting of a weighted sum of the L1 loss function, binary cross-entropy with a logit loss function and perceptual loss function. RESULTS We split the experiments into two parts. First, experiments were performed on Berkeley segmentation (BSD68) datasets corrupted by a simulated speckle. Compared with the eight existing denoising algorithms, the GAN-RW achieved the most advanced despeckling performance in terms of the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and subjective visual effect. When the noise level was 15, the average value of the GAN-RW increased by approximately 3.58% and 1.23% for PSNR and SSIM, respectively. When the noise level was 25, the average value of the GAN-RW increased by approximately 3.08% and 1.84% for PSNR and SSIM, respectively. When the noise level was 50, the average value of the GAN-RW increased by approximately 1.32% and 1.98% for PSNR and SSIM, respectively. Secondly, experiments were performed on the ultrasound images of lymph nodes, the foetal head, and the brachial plexus. The proposed method shows higher subjective visual effect when verifying on the ultrasound images. In the end, through statistical analysis, the GAN-RW achieved the highest mean rank in the Friedman test.
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Affiliation(s)
- Lun Zhang
- School of Information Science and Engineering, Yunnan University, Kunming, Yunnan, China
- Yunnan Vocational Institute of Energy Technology, Qujing, Yunnan, China
| | - Junhua Zhang
- School of Information Science and Engineering, Yunnan University, Kunming, Yunnan, China
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A novel normalization algorithm to facilitate pre-assessment of Covid-19 disease by improving accuracy of CNN and its FPGA implementation. EVOLVING SYSTEMS 2022. [PMCID: PMC8805671 DOI: 10.1007/s12530-022-09419-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
COVID-19 is still a fatal disease, which has threatened all people by affecting the human lungs. Chest X-Ray or computed tomography imaging is commonly used to make a fast and reliable medical investigation to detect the COVID-19 virus. These medical images are remarkably challenging because it is a full-time job and prone to human errors. In this paper, a new normalization algorithm that consists of Mean–Variance-Softmax-Rescale (MVSR) processes respectively is proposed to provide facilitation pre-assessment and diagnosis Covid-19 disease. In order to show the effect of MVSR normalization technique, the algorithm of proposed method is applied to chest X-ray and Sars-Cov-2 computed tomography images dataset. The normalized X-ray images with MVSR are used to recognize Covid-19 virus via Convolutional Neural Network (CNN) model. At the implementation stage, the MVSR algorithm is executed on MATLAB environment, then all the arithmetic operations of the MVSR normalization are coded in VHDL with the help of fixed-point fractional number representation format on FPGA platform. The experimental platform consists of Zynq-7000 Development FPGA Board and VGA monitor to display the both original and MVSR normalized chest X-ray images. The CNN model is constructed and executed using Anaconda Navigator interface with python language. Based on the results of this study, infections of Covid-19 disease can be easily diagnosed with MVSR normalization technique. The proposed MVSR normalization technique increased the classification accuracy of the CNN model from 83.01, to 96.16% for binary class of chest X-ray images.
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Wang S, Celebi ME, Zhang YD, Yu X, Lu S, Yao X, Zhou Q, Miguel MG, Tian Y, Gorriz JM, Tyukin I. Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects. INFORMATION FUSION 2021; 76:376-421. [DOI: 10.1016/j.inffus.2021.07.001] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
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Liu Y, Han K, Zhu YH, Zhang Y, Shen LC, Song J, Yu DJ. Improving protein fold recognition using triplet network and ensemble deep learning. Brief Bioinform 2021; 22:bbab248. [PMID: 34226918 PMCID: PMC8768454 DOI: 10.1093/bib/bbab248] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 06/04/2021] [Indexed: 12/24/2022] Open
Abstract
Protein fold recognition is a critical step toward protein structure and function prediction, aiming at providing the most likely fold type of the query protein. In recent years, the development of deep learning (DL) technique has led to massive advances in this important field, and accordingly, the sensitivity of protein fold recognition has been dramatically improved. Most DL-based methods take an intermediate bottleneck layer as the feature representation of proteins with new fold types. However, this strategy is indirect, inefficient and conditional on the hypothesis that the bottleneck layer's representation is assumed as a good representation of proteins with new fold types. To address the above problem, in this work, we develop a new computational framework by combining triplet network and ensemble DL. We first train a DL-based model, termed FoldNet, which employs triplet loss to train the deep convolutional network. FoldNet directly optimizes the protein fold embedding itself, making the proteins with the same fold types be closer to each other than those with different fold types in the new protein embedding space. Subsequently, using the trained FoldNet, we implement a new residue-residue contact-assisted predictor, termed FoldTR, which improves protein fold recognition. Furthermore, we propose a new ensemble DL method, termed FSD_XGBoost, which combines protein fold embedding with the other two discriminative fold-specific features extracted by two DL-based methods SSAfold and DeepFR. The Top 1 sensitivity of FSD_XGBoost increases to 74.8% at the fold level, which is ~9% higher than that of the state-of-the-art method. Together, the results suggest that fold-specific features extracted by different DL methods complement with each other, and their combination can further improve fold recognition at the fold level. The implemented web server of FoldTR and benchmark datasets are publicly available at http://csbio.njust.edu.cn/bioinf/foldtr/.
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Affiliation(s)
| | | | | | | | | | - Jiangning Song
- Corresponding authors: Dong-Jun Yu, School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China. E-mail: ; Jiangning Song, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia. E-mail:
| | - Dong-Jun Yu
- Corresponding authors: Dong-Jun Yu, School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China. E-mail: ; Jiangning Song, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia. E-mail:
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Depth Data Denoising in Optical Laser Based Sensors for Metal Sheet Flatness Measurement: A Deep Learning Approach. SENSORS 2021; 21:s21217024. [PMID: 34770331 PMCID: PMC8587296 DOI: 10.3390/s21217024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 10/20/2021] [Accepted: 10/21/2021] [Indexed: 11/17/2022]
Abstract
Surface flatness assessment is necessary for quality control of metal sheets manufactured from steel coils by roll leveling and cutting. Mechanical-contact-based flatness sensors are being replaced by modern laser-based optical sensors that deliver accurate and dense reconstruction of metal sheet surfaces for flatness index computation. However, the surface range images captured by these optical sensors are corrupted by very specific kinds of noise due to vibrations caused by mechanical processes like degreasing, cleaning, polishing, shearing, and transporting roll systems. Therefore, high-quality flatness optical measurement systems strongly depend on the quality of image denoising methods applied to extract the true surface height image. This paper presents a deep learning architecture for removing these specific kinds of noise from the range images obtained by a laser based range sensor installed in a rolling and shearing line, in order to allow accurate flatness measurements from the clean range images. The proposed convolutional blind residual denoising network (CBRDNet) is composed of a noise estimation module and a noise removal module implemented by specific adaptation of semantic convolutional neural networks. The CBRDNet is validated on both synthetic and real noisy range image data that exhibit the most critical kinds of noise that arise throughout the metal sheet production process. Real data were obtained from a single laser line triangulation flatness sensor installed in a roll leveling and cut to length line. Computational experiments over both synthetic and real datasets clearly demonstrate that CBRDNet achieves superior performance in comparison to traditional 1D and 2D filtering methods, and state-of-the-art CNN-based denoising techniques. The experimental validation results show a reduction in error than can be up to 15% relative to solutions based on traditional 1D and 2D filtering methods and between 10% and 3% relative to the other deep learning denoising architectures recently reported in the literature.
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Texture compensation with multi-scale dilated residual blocks for image denoising. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05920-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Liu L, Wang Z, Qiu T, Chen Q, Lu Y, Suen CY. Document image classification: Progress over two decades. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.04.114] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Alla Takam C, Tchagna Kouanou A, Samba O, Mih Attia T, Tchiotsop D. Big Data Framework Using Spark Architecture for Dose Optimization Based on Deep Learning in Medical Imaging. ARTIF INTELL 2021. [DOI: 10.5772/intechopen.97746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Deep learning and machine learning provide more consistent tools and powerful functions for recognition, classification, reconstruction, noise reduction, quantification and segmentation in biomedical image analysis. Some breakthroughs. Recently, some applications of deep learning and machine learning for low-dose optimization in computed tomography have been developed. Due to reconstruction and processing technology, it has become crucial to develop architectures and/or methods based on deep learning algorithms to minimize radiation during computed tomography scan inspections. This chapter is an extension work done by Alla et al. in 2020 and explain that work very well. This chapter introduces the deep learning for computed tomography scan low-dose optimization, shows examples described in the literature, briefly discusses new methods for computed tomography scan image processing, and provides conclusions. We propose a pipeline for low-dose computed tomography scan image reconstruction based on the literature. Our proposed pipeline relies on deep learning and big data technology using Spark Framework. We will discuss with the pipeline proposed in the literature to finally derive the efficiency and importance of our pipeline. A big data architecture using computed tomography images for low-dose optimization is proposed. The proposed architecture relies on deep learning and allows us to develop effective and appropriate methods to process dose optimization with computed tomography scan images. The real realization of the image denoising pipeline shows us that we can reduce the radiation dose and use the pipeline we recommend to improve the quality of the captured image.
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