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Duong MT, Nguyen Thi BT, Lee S, Hong MC. Multi-Branch Network for Color Image Denoising Using Dilated Convolution and Attention Mechanisms. SENSORS (BASEL, SWITZERLAND) 2024; 24:3608. [PMID: 38894398 PMCID: PMC11175289 DOI: 10.3390/s24113608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 05/28/2024] [Accepted: 05/31/2024] [Indexed: 06/21/2024]
Abstract
Image denoising is regarded as an ill-posed problem in computer vision tasks that removes additive noise from imaging sensors. Recently, several convolution neural network-based image-denoising methods have achieved remarkable advances. However, it is difficult for a simple denoising network to recover aesthetically pleasing images owing to the complexity of image content. Therefore, this study proposes a multi-branch network to improve the performance of the denoising method. First, the proposed network is designed based on a conventional autoencoder to learn multi-level contextual features from input images. Subsequently, we integrate two modules into the network, including the Pyramid Context Module (PCM) and the Residual Bottleneck Attention Module (RBAM), to extract salient information for the training process. More specifically, PCM is applied at the beginning of the network to enlarge the receptive field and successfully address the loss of global information using dilated convolution. Meanwhile, RBAM is inserted into the middle of the encoder and decoder to eliminate degraded features and reduce undesired artifacts. Finally, extensive experimental results prove the superiority of the proposed method over state-of-the-art deep-learning methods in terms of objective and subjective performances.
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Affiliation(s)
- Minh-Thien Duong
- Department of Information and Telecommunication Engineering, Soongsil University, Seoul 06978, Republic of Korea; (M.-T.D.); (B.-T.N.T.)
| | - Bao-Tran Nguyen Thi
- Department of Information and Telecommunication Engineering, Soongsil University, Seoul 06978, Republic of Korea; (M.-T.D.); (B.-T.N.T.)
| | - Seongsoo Lee
- Department of Intelligent Semiconductor, Soongsil University, Seoul 06978, Republic of Korea;
| | - Min-Cheol Hong
- School of Electronic Engineering, Soongsil University, Seoul 06978, Republic of Korea
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Qiao C, Zeng Y, Meng Q, Chen X, Chen H, Jiang T, Wei R, Guo J, Fu W, Lu H, Li D, Wang Y, Qiao H, Wu J, Li D, Dai Q. Zero-shot learning enables instant denoising and super-resolution in optical fluorescence microscopy. Nat Commun 2024; 15:4180. [PMID: 38755148 PMCID: PMC11099110 DOI: 10.1038/s41467-024-48575-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 05/07/2024] [Indexed: 05/18/2024] Open
Abstract
Computational super-resolution methods, including conventional analytical algorithms and deep learning models, have substantially improved optical microscopy. Among them, supervised deep neural networks have demonstrated outstanding performance, however, demanding abundant high-quality training data, which are laborious and even impractical to acquire due to the high dynamics of living cells. Here, we develop zero-shot deconvolution networks (ZS-DeconvNet) that instantly enhance the resolution of microscope images by more than 1.5-fold over the diffraction limit with 10-fold lower fluorescence than ordinary super-resolution imaging conditions, in an unsupervised manner without the need for either ground truths or additional data acquisition. We demonstrate the versatile applicability of ZS-DeconvNet on multiple imaging modalities, including total internal reflection fluorescence microscopy, three-dimensional wide-field microscopy, confocal microscopy, two-photon microscopy, lattice light-sheet microscopy, and multimodal structured illumination microscopy, which enables multi-color, long-term, super-resolution 2D/3D imaging of subcellular bioprocesses from mitotic single cells to multicellular embryos of mouse and C. elegans.
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Affiliation(s)
- Chang Qiao
- Department of Automation, Tsinghua University, 100084, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China
- Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography, Tsinghua University, 100084, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, 100010, Beijing, China
| | - Yunmin Zeng
- Department of Automation, Tsinghua University, 100084, Beijing, China
| | - Quan Meng
- National Laboratory of Biomacromolecules, New Cornerstone Science Laboratory, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101, Beijing, China
- College of Life Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Xingye Chen
- Department of Automation, Tsinghua University, 100084, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China
- Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography, Tsinghua University, 100084, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, 100010, Beijing, China
- Research Institute for Frontier Science, Beihang University, 100191, Beijing, China
| | - Haoyu Chen
- National Laboratory of Biomacromolecules, New Cornerstone Science Laboratory, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101, Beijing, China
- College of Life Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Tao Jiang
- National Laboratory of Biomacromolecules, New Cornerstone Science Laboratory, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101, Beijing, China
- College of Life Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Rongfei Wei
- National Laboratory of Biomacromolecules, New Cornerstone Science Laboratory, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101, Beijing, China
| | - Jiabao Guo
- National Laboratory of Biomacromolecules, New Cornerstone Science Laboratory, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101, Beijing, China
- College of Life Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Wenfeng Fu
- National Laboratory of Biomacromolecules, New Cornerstone Science Laboratory, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101, Beijing, China
- College of Life Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Huaide Lu
- National Laboratory of Biomacromolecules, New Cornerstone Science Laboratory, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101, Beijing, China
- College of Life Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Di Li
- National Laboratory of Biomacromolecules, New Cornerstone Science Laboratory, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101, Beijing, China
| | - Yuwang Wang
- Beijing National Research Center for Information Science and Technology, Tsinghua University, 100084, Beijing, China
| | - Hui Qiao
- Department of Automation, Tsinghua University, 100084, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China
- Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography, Tsinghua University, 100084, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, 100010, Beijing, China
| | - Jiamin Wu
- Department of Automation, Tsinghua University, 100084, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China
- Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography, Tsinghua University, 100084, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, 100010, Beijing, China
| | - Dong Li
- National Laboratory of Biomacromolecules, New Cornerstone Science Laboratory, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101, Beijing, China.
- College of Life Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China.
| | - Qionghai Dai
- Department of Automation, Tsinghua University, 100084, Beijing, China.
- Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China.
- Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography, Tsinghua University, 100084, Beijing, China.
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, 100010, Beijing, China.
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