Brightness and contrast controllable image enhancement based on histogram specification

Computer-Aided Assessment of Repigmentation Rates in Vitiligo Patients: Implications for Treatment Efficacy - A Retrospective Study

Precise evaluation of repigmentation in vitiligo patients is crucial for monitoring treatment efficacy and enhancing patient satisfaction. This study aimed to develop a computer-aided system for assessing repigmentation rates in vitiligo patients, providing valuable insights for clinical practice. A retrospective study was conducted at the Dermatology Department of Shenzhen People's Hospital between June 2019 and November 2022. Pre- and post-treatment images of vitiligo lesions under Wood's lamp were collected, involving 833 participants stratified by sex, age, and pigmentation patterns. Our results demonstrated that the marginal pigmentation pattern exhibited a higher repigmentation rate of 72% compared with the central non-follicular pattern at 45%. Males had a slightly higher average repigmentation rate of 0.37 in comparison to females at 0.33. Among age groups, individuals aged 0-20 years showed the highest average repigmentation rate at 0.41, while the oldest age group (61-80 years) displayed the lowest rate at 0.25. Analysis of multiple visits identified the marginal pattern as the most prevalent (60%), with a mean repigmentation rate of 40%. This study introduced a computational system for evaluating vitiligo repigmentation rates, enhancing our comprehension of patient responses, and ultimately contributing to enhanced clinical care.

Learning Controllable ISP for Image Enhancement

We present a plug-and-play Image Signal Processor (ISP) for image enhancement to better produce diverse image styles than the previous works. Our proposed method, ContRollable Image Signal Processor (CRISP), explicitly controls the parameters of the ISP that determine output image styles. ISP parameters for high-quality (HQ) image styles are encoded into low-dimensional latent codes, allowing fast and easy style adjustments. We empirically show that CRISP covers a wide range of image styles with high efficiency. On the MIT-Adobe FiveK dataset, CRISP can very closely estimate the reference styles produced by human experts and achieves better MOS with diverse image styles. Compared with the state-of-the-art method, our ISP comprises only 19 parameters, allowing CRISP to have 2× smaller parameters and 100× reduced FLOPs for an image output. CRISP outperforms previous works in PSNR and FLOPs with several scenarios for style adjustments.

Is histogram manipulation always beneficial when trying to improve model performance across devices? Experiments using a Meibomian gland segmentation model

Meibomian gland dysfunction (MGD) is caused by abnormalities of the meibomian glands (MG) and is one of the causes of evaporative dry eye (DED). Precise MG segmentation is crucial for MGD-related DED diagnosis because the morphological parameters of MG are of importance. Deep learning has achieved state-of-the-art performance in medical image segmentation tasks, especially when training and test data come from the same distribution. But in practice, MG images can be acquired from different devices or hospitals. When testing image data from different distributions, deep learning models that have been trained on a specific distribution are prone to poor performance. Histogram specification (HS) has been reported as an effective method for contrast enhancement and improving model performance on images of different modalities. Additionally, contrast limited adaptive histogram equalization (CLAHE) will be used as a preprocessing method to enhance the contrast of MG images. In this study, we developed and evaluated the automatic segmentation method of the eyelid area and the MG area based on CNN and automatically calculated MG loss rate. This method is evaluated in the internal and external testing sets from two meibography devices. In addition, to assess whether HS and CLAHE improve segmentation results, we trained the network model using images from one device (internal testing set) and tested on images from another device (external testing set). High DSC (0.84 for MG region, 0.92 for eyelid region) for the internal test set was obtained, while for the external testing set, lower DSC (0.69-0.71 for MG region, 0.89-0.91 for eyelid region) was obtained. Also, HS and CLAHE were reported to have no statistical improvement in the segmentation results of MG in this experiment.

High-resolution polarization-sensitive Fourier ptychography microscopy using a high numerical aperture dome illuminator

Polarization-sensitive Fourier-ptychography microscopy (pFPM) allows for high resolution imaging while maintaining a large field of view, and without mechanical movements of optical-setup components. In contrast to ordinary light microscopes, pFPM provides quantitative absorption and phase information, for complex and birefringent specimens, with high resolution across a wide field of view. Using a semi-spherical home-built LED illumination array, a single polarizer, and a 10x /0.28NA objective, we experimentally demonstrate high performance pFPM with a synthesized NA of 1.1. Applying the standard quantitative method, a measured half-pitch resolution of 244 nm is achieved for the 1951 USAF resolution test target. As application examples, the polarimetric properties of a herbaceous flowering plant and the metastatic carcinoma of human liver cells are analyzed and quantitatively imaged.

X-ray Image Enhancement Based on Nonsubsampled Shearlet Transform and Gradient Domain Guided Filtering

In this paper, we propose an image enhancement algorithm combining non-subsampled shearlet transform and gradient-domain guided filtering to address the problems of low resolution, noise amplification, missing details, and weak edge gradient retention in the X-ray image enhancement process. First, we decompose histogram equalization and nonsubsampled shearlet transform to the original image. We get a low-frequency sub-band and several high-frequency sub-bands. Adaptive gamma correction with weighting distribution is used for the low-frequency sub-band to highlight image contour information and improve the overall contrast of the image. The gradient-domain guided filtering is conducted for the high-frequency sub-bands to suppress image noise and highlight detail and edge information. Finally, we reconstruct all the effectively processed sub-bands by the inverse non-subsampled shearlet transform and obtain the final enhanced image. The experimental results show that the proposed algorithm has good results in X-ray image enhancement, and its objective index also has evident advantages over some classical algorithms.

Image Enhancement Based on Rough Set and Fractional Order Differentiator

In the paper, an image enhancement algorithm based on a rough set and fractional order differentiator is proposed. By combining the rough set theory with a Gaussian mixture model, a new image segmentation algorithm with higher immunity is obtained. This image segmentation algorithm can obtain more image layers with concentrating information and preserve more image details than traditional algorithms. After preprocessing, the segmentation layers will be enhanced by a new adaptive fractional order differential mask in the Fourier domain. Experimental results and numerical analysis have verified the effectiveness of the proposed algorithm.

A Methodology for Extracting Power-Efficient and Contrast Enhanced RGB Images

Smart devices have become an integral part of people's lives. The most common activities for users of such smart devices that are energy sources are voice calls, text messages (SMS) or email, browsing the World Wide Web, streaming audio/video, and using sensor devices such as cameras, GPS, Wifi, 4G/5G, and Bluetooth either for entertainment or for the convenience of everyday life. In addition, other power sources are the device screen, RAM, and CPU. The need for communication, entertainment, and computing makes the optimal management of the power consumption of these devices crucial and necessary. In this paper, we employ a computationally efficient linear mapping algorithm known as Concurrent Brightness & Contrast Scaling (CBCS), which transforms the initial intensity value of the pixels in the YC_bC_r color system. We introduce a methodology that gives the user the opportunity to select a picture and modify it using the suggested algorithm in order to make it more energy-friendly with or without the application of a histogram equalization (HE). The experimental results verify the efficacy of the presented methodology through various metrics from the field of digital image processing that contribute to the choice of the optimal values for the parameters a,b that meet the user's preferences (low or high-contrast images) and green power. For both low-contrast and low-power images, the histogram equalization should be omitted, and the appropriate a should be much lower than one. To create high-contrast and low-power images, the application of HE is essential. Finally, quantitative and qualitative evaluations have shown that the proposed approach can achieve remarkable performance.

Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed

Aiming at the problem of the poor robustness of existing methods to deal with diverse industrial weld image data, we collected a series of asymmetric laser weld images in the largest laser equipment workshop in Asia, and studied these data based on an industrial image processing algorithm and deep learning algorithm. The median filter was used to remove the noises in weld images. The image enhancement technique was adopted to increase the image contrast in different areas. The deep convolutional neural network (CNN) was employed for feature extraction; the activation function and the adaptive pooling approach were improved. Transfer Learning (TL) was introduced for defect detection and image classification on the dataset. Finally, a deep learning-based model was constructed for weld defect detection and image recognition. Specific instance datasets verified the model’s performance. The results demonstrate that this model can accurately identify weld defects and eliminate the complexity of manually extracting features, reaching a recognition accuracy of 98.75%. Hence, the reliability and automation of detection and recognition are improved significantly. The research results can provide a theoretical and practical reference for the defect detection of sheet metal laser welding and the development of the industrial laser manufacturing industry.

No-reference quality assessment for image-based assessment of economically important tropical woods

Image Quality Assessment (IQA) is essential for the accuracy of systems for automatic recognition of tree species for wood samples. In this study, a No-Reference IQA (NR-IQA), wood NR-IQA (WNR-IQA) metric was proposed to assess the quality of wood images. Support Vector Regression (SVR) was trained using Generalized Gaussian Distribution (GGD) and Asymmetric Generalized Gaussian Distribution (AGGD) features, which were measured for wood images. Meanwhile, the Mean Opinion Score (MOS) was obtained from the subjective evaluation. This was followed by a comparison between the proposed IQA metric, WNR-IQA, and three established NR-IQA metrics, namely Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), deepIQA, Deep Bilinear Convolutional Neural Networks (DB-CNN), and five Full Reference-IQA (FR-IQA) metrics known as MSSIM, SSIM, FSIM, IWSSIM, and GMSD. The proposed WNR-IQA metric, BRISQUE, deepIQA, DB-CNN, and FR-IQAs were then compared with MOS values to evaluate the performance of the automatic IQA metrics. As a result, the WNR-IQA metric exhibited a higher performance compared to BRISQUE, deepIQA, DB-CNN, and FR-IQA metrics. Highest quality images may not be routinely available due to logistic factors, such as dust, poor illumination, and hot environment present in the timber industry. Moreover, motion blur could occur due to the relative motion between the camera and the wood slice. Therefore, the advantage of WNR-IQA could be seen from its independency from a “perfect” reference image for the image quality evaluation.

A novel framework for MR image segmentation and quantification by using MedGA

BACKGROUND AND OBJECTIVES

Image segmentation represents one of the most challenging issues in medical image analysis to distinguish among different adjacent tissues in a body part. In this context, appropriate image pre-processing tools can improve the result accuracy achieved by computer-assisted segmentation methods. Taking into consideration images with a bimodal intensity distribution, image binarization can be used to classify the input pictorial data into two classes, given a threshold intensity value. Unfortunately, adaptive thresholding techniques for two-class segmentation work properly only for images characterized by bimodal histograms. We aim at overcoming these limitations and automatically determining a suitable optimal threshold for bimodal Magnetic Resonance (MR) images, by designing an intelligent image analysis framework tailored to effectively assist the physicians during their decision-making tasks.

METHODS

In this work, we present a novel evolutionary framework for image enhancement, automatic global thresholding, and segmentation, which is here applied to different clinical scenarios involving bimodal MR image analysis: (i) uterine fibroid segmentation in MR guided Focused Ultrasound Surgery, and (ii) brain metastatic cancer segmentation in neuro-radiosurgery therapy. Our framework exploits MedGA as a pre-processing stage. MedGA is an image enhancement method based on Genetic Algorithms that improves the threshold selection, obtained by the efficient Iterative Optimal Threshold Selection algorithm, between the underlying sub-distributions in a nearly bimodal histogram.

RESULTS

The results achieved by the proposed evolutionary framework were quantitatively evaluated, showing that the use of MedGA as a pre-processing stage outperforms the conventional image enhancement methods (i.e., histogram equalization, bi-histogram equalization, Gamma transformation, and sigmoid transformation), in terms of both MR image enhancement and segmentation evaluation metrics.

CONCLUSIONS

Thanks to this framework, MR image segmentation accuracy is considerably increased, allowing for measurement repeatability in clinical workflows. The proposed computational solution could be well-suited for other clinical contexts requiring MR image analysis and segmentation, aiming at providing useful insights for differential diagnosis and prognosis.

Adaptive Contrast Enhancement for Infrared Images Based on the Neighborhood Conditional Histogram

In this paper, an adaptive contrast enhancement method based on the neighborhood conditional histogram is proposed to improve the visual quality of thermal infrared images. Existing block-based local contrast enhancement methods usually suffer from the over-enhancement of smooth regions or the loss of some details. To address these drawbacks, we first introduce a neighborhood conditional histogram to adaptively enhance the contrast and avoid the over-enhancement caused by the original histogram. Then the clip-redistributed histogram of the contrast-limited adaptive histogram equalization (CLAHE) is replaced by the neighborhood conditional histogram. In addition, the local mapping function of each sub-block is updated based on the global mapping function to further eliminate the block artifacts. Lastly, the optimized local contrast enhancement process, which combines both global and local enhanced results is employed to obtain the desired enhanced result. Experiments are conducted to evaluate the performance of the proposed method and the other five methods are introduced as a comparison. Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms the other block-based methods on local contrast enhancement, visual quality improvement, and noise suppression.

Optimized Contrast Enhancement for Infrared Images Based on Global and Local Histogram Specification

In this paper, an optimized contrast enhancement method combining global and local enhancement results is proposed to improve the visual quality of infrared images. Global and local contrast enhancement methods have their merits and demerits, respectively. The proposed method utilizes the complementary characteristics of these two methods to achieve noticeable contrast enhancement without artifacts. In our proposed method, the 2D histogram, which contains both global and local gray level distribution characteristics of the original image, is computed first. Then, based on the 2D histogram, the global and local enhanced results are obtained by applying histogram specification globally and locally. Lastly, the enhanced result is computed by solving an optimization equation subjected to global and local constraints. The pixel-wise regularization parameters for the optimization equation are adaptively determined based on the edge information of the original image. Thus, the proposed method is able to enhance the local contrast while preserving the naturalness of the original image. Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms the block-based methods for improving the visual quality of infrared images.