Contrast Enhancement Algorithm Research Articles

Comparison of algorithms for contrast enhancement based on triangle orientation discrimination assessments by convolutional neural networks

Within the last decades, a large number of techniques for contrast enhancement has been proposed. There are some comparisons of such algorithms for few images and figures of merit. However, many of these figures of merit cannot assess usability of altered image content for specific tasks, such as object recognition. In this work, the effect of contrast enhancement algorithms is evaluated by means of the triangle orientation discrimination (TOD), which is a current method for imager performance assessment. The conventional TOD approach requires observers to recognize equilateral triangles pointing in four different directions, whereas here convolutional neural network models are used for the classification task. These models are trained by artificial images with single triangles. Many methods for contrast enhancement highly depend on the content of the entire image. Therefore, the images are superimposed over natural backgrounds with varying standard deviations to provide different signal-to-background ratios. Then, these images are degraded by Gaussian blur and noise representing degradational camera effects and sensor noise. Different algorithms, such as the contrast-limited adaptive histogram equalization or local range modification, are applied. Then accuracies of the trained models on these images are compared for different contrast enhancement algorithms. Accuracy gains for low signal-to-background ratios and sufficiently large triangles are found, whereas impairments are found for high signal-to-background ratios and small triangles. A high generalization ability of our TOD model is found from the similar accuracies for several image databases used for backgrounds. Finally, implications of replacing triangles with real target signatures when using such advanced digital signal processing algorithms are discussed. The results are a step toward the assessment of those algorithms for generic target recognition.

Underwater Image Enhancement for Instance Segmentation using Deep Learning Models

Underwater instance segmentation greatly depends on color-blended underwater images. In this work, a combination of Generalized Color Fourier Descriptor (GCFD), Convolutional Neural Network (CNN) and Mask Region-based Convolutional Neural Network (Mask R-CNN) models were employed to generate a mask for each bounding-boxed Region of Interest (ROI) to obtain enhanced individual underwater segmented images from their complex background accurately. By this image enhancement approach, individual underwater instances are segmented from their complex background accurately. The Patch-based Contrast Quality Index (PCQI) evaluation of our proposed image enhancement method (GCFD) after conducting experiment on the employed datasets shows performance accuracy of 1.1336, which is higher than the 1.1126 performance accuracy achieved by the Contrast-enhancement Algorithm (CA).

Analysis and Comparison of Image Enhancement Technique for Improving PSNR of Lung Images by Linear Contrast Enhancement Technique over Histogram Equalization Technique

Aim: The aim of this study is to compare and analyze linear contrast enhancement algorithm for lung image enhancement over novel histogram equalization technique. Material and Methods: In this research, different sources of lung images collected from the kaggle website were used. Samples were considered as (N=30) for linear contrast filter and (N=30) for novel histogram equalization technique with total sample size calculated using clinical.com. As a result the total number of samples was calculated to be 60. Using SPSS Software and a standard data set, the PSNR was obtained. Both linear contrast filter and novel histogram equalization technique image enhancement were implemented on lung images through Matlab coding and also PSNR values of each image were extracted. Then through SPSS software comparison and analysis has been made. Result: In the final output of image enhancement, novel histogram equalization technique shows better performance in improving PSNR of lung images than linear contrast filter. Comparison of PSNR values are done by independent sample test using IBM-SPSS software. There is a statistical difference between histogram technique and linear contrast filter. The novel histogram equalization technique showed higher results of PSNR (65.9197dB) with (p=0.04) in comparison with linear contrast filter (36.1190dB). Conclusion: Within this research study the histogram equalization image enhancement technique has greater PSNR value of lung images than in linear contrast enhancement technique.

Polarization Imaging Method for Underwater Low-Visibility Metal Target Using Focus Dividing Plane

Aiming at the problems of brightness attenuation and contrast reduction in the target image caused by underwater low-visibility environments, a metal target detection method based on split-focus plane polarization imaging is proposed. Firstly, a hybrid enhancement method is proposed to clarify the degraded polarization image. In this study, the GrayWorld method is improved to compensate the attenuation difference of the total light intensity of the polarization image. Variational contrast and saturation enhancement algorithms are used to reduce the underwater scattering effect; secondly, a split-focus plane polarization imaging system is built to complete the control of camera parameters, polarization image acquisition and information processing. Under different underwater low-visibility conditions, polarization imaging of targets with different materials can be realized; finally, an image quality evaluation system is constructed to compare the light intensity and degree of polarization images that are collected by the focal plane polarization imaging system. The polarization characteristics of metal and nonmetal target plates are analyzed. The results show that under the condition of low visibility, the obtained polarization image contrast of the metal target is relatively high, and its EME, information entropy and average gradient are increased by 183.82%, 53.46% and 586.22% on average relative to the image of light intensity. In an underwater low-visibility scene, the method of focal plane polarization imaging proposed in this paper can reduce the difficulty of metal target detection.

Lightweight Separable Convolution Network for Breast Cancer Histopathological Identification.

Breast cancer is one of the leading causes of death among women worldwide. Histopathological images have proven to be a reliable way to find out if someone has breast cancer over time, however, it could be time consuming and require much resources when observed physically. In order to lessen the burden on the pathologists and save lives, there is need for an automated system to effectively analysis and predict the disease diagnostic. In this paper, a lightweight separable convolution network (LWSC) is proposed to automatically learn and classify breast cancer from histopathological images. The proposed architecture aims to treat the problem of low quality by extracting the visual trainable features of the histopathological image using a contrast enhancement algorithm. LWSC model implements separable convolution layers stacked in parallel with multiple filters of different sizes in order to obtain wider receptive fields. Additionally, the factorization and the utilization of bottleneck convolution layers to reduce model dimension were introduced. These methods reduce the number of trainable parameters as well as the computational cost sufficiently with greater non-linear expressive capacity than plain convolutional networks. The evaluation results depict that the proposed LWSC model performs optimally, obtaining 97.23% accuracy, 97.71% sensitivity, and 97.93% specificity on multi-class categories. Compared with other models, the proposed LWSC obtains comparable performance.

Hybrid enhanced whale optimization algorithm for contrast and detail enhancement of color images

Hybrid enhanced whale optimization algorithm for contrast and detail enhancement of color images

The defect detection of 3D-printed ceramic curved surface parts with low contrast based on deep learning

Some defects were produced during 3D printing ceramic parts and it is difficult to detect these defects, especially for the ceramic curved surface parts, because of low contrast between defects and ceramic background, blurred edges and low efficiency. In this word, a defect detection method with low contrast based on deep learning is studied. Firstly, the difficulties and adverse effects that occur in the detection of ceramic curved surface parts are analyzed qualitatively. Then, a blurry inpainting network model is proposed to effectively reduce the degree of blurring on the curved surface. A multi-scale detail contrast enhancement algorithm is also established to solve the problems low contrast among defective and the background regions, which can highlight the characteristic information of the defect regions. On this basis, two types of defects in the curved surface parts are detected by using our constructed ECANet-Mobilenet SSD network model. The results show that the prediction accuracy for crack and bulge defects recognition can reach 94.35%, and 96.72%, respectively, and meanwhile, their average detection time of a single image is 0.78s. Therefore, this study on the defect detection of 3D-printed ceramic curved surface parts can contribute to the intelligent and 3D printing development of advanced ceramic industry.

Detection of stress, anxiety and depression (SAD) in video surveillance using ResNet-101

Emotional disruptions are associated with the psychological state of a person that comes out in the form of non-verbal signals. The usage of medical resources for the identification of emotional activities is a complex and expensive task. Computer vision techniques equipped with artificial intelligence are capable of bringing automatic and fast identification of emotional variations of the human mind. Emotional variations may contain overlapping stages in which multiple non-separable emotional symptoms are more difficult to classify. The objective is to draw up an investigation of such a non-verbal body signal and correlate it with the psychological state of the person. Artificial intelligence techniques explore the identification of psychological states using pixel intensity information from datasets of facial expressions. The proposed study explores the classification of emotional symptoms into stress, anxiety and depression from facial expressions in a real-time video surveillance dataset. The second objective of the proposed study is to maintain classification accuracy for variation of real-time noise that may distort feature information. The study exhibits the use of the Kalman filter for the localization of intensity-based features and the use of the bilateral filter, contrast enhancement and adaptive filter algorithms for the removal of noise. Finally, ResNet 101 architecture has been used to classify symptoms of stress, anxiety and depression. The robustness of the proposed classification algorithm has been compared with other algorithms, such as PCA, Gradient boosting algorithm, KNN, Decision tree, Naïve Bayes, and SVM. It has been observed that ResNet 101 outperformed other models with a notable 98.4% accuracy.

Dark stereo

It is often desirable or unavoidable to display Virtual Reality (VR) or stereoscopic content at low brightness. For example, a dimmer display reduces the flicker artefacts that are introduced by low-persistence VR headsets. It also saves power, prolongs battery life, and reduces the cost of a display or projection system. Additionally, stereo movies are usually displayed at relatively low luminance due to polarization filters or other optical elements necessary to separate two views. However, the binocular depth cues become less reliable at low luminance. In this paper, we propose a model of stereo constancy that predicts the precision of binocular depth cues for a given contrast and luminance. We use the model to design a novel contrast enhancement algorithm that compensates for the deteriorated depth perception to deliver good-quality stereoscopic images even for displays of very low brightness.

CONTRAST ENHANCEMENT OF SCANNING ELECTRON MICROSCOPY IMAGES USING A NONCOMPLEX MULTIPHASE ALGORITHM

Microscopic technology has recently flourished, allowing unparalleled viewing of microscopic elements invisible to the normal eye. Still, the existence of unavoidable constraints led on many occasions to have low contrast scanning electron microscopic (SEM) images. Thus, a noncomplex multiphase (NM) algorithm is proposed in this study to provide better contrast for various SEM images. The developed algorithm contains the following stages: first, the intensities of the degraded image are modified using a two-step regularization procedure. Next, a gamma-corrected cumulative distribution function of the logarithmic uniform distribution approach is applied for contrast enhancement. Finally, an automated histogram expansion technique is used to redistribute the pixels of the image properly. The NM algorithm is applied to natural-contrast distorted SEM images, as well as its results are compared with six algorithms with different processing notions. To assess the quality of images, three modern metrics are utilized, in that each metric measures the quality based on unique aspects. Extensive appraisals revealed the adequate processing abilities of the NM algorithm, as it can process many images suitably and its performances outperformed many available contrast enhancement algorithms in different aspects.

A novel detail weighted histogram equalization method for brightness preserving image enhancement based on partial statistic and global mapping model

Histogram equalization (HE) is a classic and widely used image contrast enhancement algorithm for its good performance and high efficiency. However, over-enhancement caused by high peak in the histogram affects the subjective quality of the image processed by HE to a large extent. In this paper, a detail weighted histogram equalization (DWHE) method is proposed based on a novel histogram modification (HM) model named partial statistic and global mapping (PSGM) to alleviate high peak and suppress over-enhancement. Moreover, the authors implement a refined version of gamma correction (GC) named texture enhancement function (TEF) on high-frequency images to reduce the noise amplification effect. At last, the authors propose a novel adaptively weighted pixel-level image fusion method to further reduce the phenomenon of over-enhancement and improve brightness distribution. Both subjective and quantitative evaluations are conducted on images containing a variety of scenes. Compared with several state-of-the-art image enhancement methods, the proposed framework obtained generally the best performance in aspects of both subjective appearance and objective evaluation indices. Therefore, it is proved that the proposed methods can effectively alleviate the over-enhancement, enrich image details, and efficiently improve the visual quality while preserving the brightness of the image.

Analysis of Combination Algorithms for Denoising and Contrast Enhancement Images

Reducing noise and increasing image contrast is part of the purpose of enhancing image quality; instead, it will impact change the diversity of information in the image based on the Shannon entropy value. Decrease quality caused by noise salt and pepper in this research or abnormal contrast in the image causes objects in the image to become unclear. Low contrast has a major impact on image quality, including noise reduction processes affecting image information so that the quality of the reduced image becomes something to consider for large noise. Iterative Denoising and Backward Projections with CNN (IDBP-CNN) and Different Applied Median Filter (DAMF) is a good solution for denoising a large percentage of noise with good quality results image. In other research for contrast enhancement, Triangular Fuzzy Membership-Contrast Limited Adaptive Histogram Equalization (TFM-CLAHE) and Adaptive Fuzzy Contrast Enhancement Algorithm with Details Preserving (AFCEDP) is claimed to a good solution to solve low contrast of the image. Therefore, this study is to find the best combination of denoising and contrast enhancement to get good image results with step denoising followed by contrast enhancement. Based on the experimental testing is got the best combination is the DAMF + AFCEDP algorithm with an average of PSNR 35dB and an average difference Shannon entropy of 0.0130.

Image contrast enhancement algorithm for X-ray observation of space materials in situ

Chinese Space Station has planned a high-temperature material science experiment rack, equipped with an X-ray projection imaging module, to support the development of material experiments and research in space. BiFeO3 has been selected as the first batch of experimental materials for Chinese Space Station. The melting and solidification process of BiFeO3, an opaque, high-temperature material, is observed by X-ray observation module in situ. X-ray is the dominant way to observe opaque materials due to its penetrability. In-situ observation of materials is the top priority of this study, so we have strict requirements on image quality, and high-quality images can better analyze the properties and properties of materials. Limited by narrow size and high temperature conditions, the X-ray images collected have low contrast, serious noise pollution, and poor imaging quality. To enhance the contrast and improve the edge details of such images, a grayscale weighted histogram equalization combined with high-frequency enhancement (GWHE-HFE) algorithm is proposed. First, we add a mask to the input image to obtain the region of interest (ROI), and then filter out the low-frequency components of the image by Gaussian high-pass filter to preserve high-frequency details. Second, the image obtained in the previous and the X-ray image of ROI are respectively multiplied by a coefficient and added to obtain the edge-emphasized X-ray image. And then, we use grayscale weighted histogram equalization (GWHE) to process the image obtained in the second step to obtain the contrast enhanced X-ray image. The enhanced image shows the crystal grains and the thin bands where the solid and the melt intersect, and it is helpful to accurately locate the solid solution interface. Experiments on X-ray images of BiFeO3 growth demonstrate that this combined method outperforms existing ones both qualitatively and quantitatively, providing an in-depth and effective analysis method for in high-temperature material-science experiments.

Unsupervised and optimized thermal image quality enhancement and visual surveillance applications

Thermal images suffer from low-luminance issues under specific conditions, such as heat radiation, distance-to-radiated objects, reflection angles. Low-luminance thermal images degrade the performance of a vision-based system. Present thermal contrast enhancement algorithms usually tolerate the background, noise amplification, and brightness distortion from over-enhancement. This paper introduces; i) an image enhancement algorithm for thermal satellite and aerial images; ii) a novel thermal image quality assessment; iii) a modified nature-inspired algorithm to search optimal parameter for the proposed enhancement algorithm; and iv) the application of the photovoltaic panel inspection by using the proposed algorithms. Experimental results thermal photovoltaic panel images with various luminance scenes from two datasets demonstrate the proposed method’s effectiveness compared with state-of-the-art algorithms both qualitatively and quantitatively. Moreover, the proposed algorithm performs favorably for low-luminance thermal images with different illumination levels while enhancing the dark regions’ details and preserving mean-brightness.

A contrast enhancement framework under uncontrolled environments based on just noticeable difference

Image contrast enhancement refers to an operation of remapping the pixels’ values of an image to emphasize desired information in the image. In this work, we propose a novel pixel-based (local) contrast enhancement algorithm, based on the human visual perception. First, we make an observation that pixels with lower regional contrast should be amplified for the purpose of enhancing the contrast and pixels with higher regional contrast should be suppressed to avoid undesired over-enhancement. To determine the quality of the regional contrast in the image (either lower or higher), a reference image will be created using a proposed global based contrast enhancement method (termed as Mean Brightness Bidirectional Histogram Equalization in the paper) for fast computation reason. To quantify the abovementioned regional contrast, we propose a method based on human visual perception taking Just Noticeable Difference (JND) into account. In short, our proposed algorithm is able to limit the enhancement of well-contrasted regions and enhance the poor contrast regions in an image. Both objective quality and subjective quality experimental results suggested that the proposed algorithm enhances images consistently across images with different dynamic range. We conclude that the proposed algorithm exhibits excellent consistency in producing satisfactory result for different type of images. It is important to note that the algorithm can be directly implemented in color space and not limited only to grayscale. The proposed algorithm can be obtained from the following GitHub link: https://github.com/UTARSL1/CHE.

Probabilistic Modeling of Histogram Based Fuzzy Image Enhancement Algorithm

Probabilistic model checking is frequently used to examine stochastic behavior. Model-checking is important for examining before extensive simulations, in differing fields, including computer communications, networks, security, and biology. In this work performance of histogram-based fuzzy image enhancement (HFIE Algorithm). The fuzzy contrast enhancement algorithm based on histograms is used to improve low contrast colored photographs. K is the contrast intensification parameter calculated from the histogram. The RGB image is transformed into HSV to preserve the chromatic information in the original image. To improve the image, only the V component is stretched using the M and K parameters. HFIE Algorithm is analyzed by developing its Discrete-time Markov Chain (DTMC) model in the Probabilistic Symbolic Model Checker (PRISM). First, a labeled transition diagram showing the functionality of HFIE Algorithm is constructed, then the HFIE Algorithm model using the PRISM tool is developed. The expected time to convert RGB image to HSV, probability of achieving enhanced RGB image, and expected SNR are measured with the help of properties.

Velostat Sensor Array for Object Recognition

This paper presents a cost-effective pressure sensing system for object detection and identification. The pressure sensing system consists of a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$27\times 27$ </tex-math></inline-formula> piezoresistive sensor array made of carbon composite Velostat, a signal processing subsystem for signal scanning, amplification, registration, and enhancement. A convolutional neural network is used to classify various objects through the pressure signals produced and processed by the sensing array. Based on systematic characterizations and calibrations of sensing materials and system sensitivity, three experiment setups are established to recognize 10 objects to be detected. In series of experiments, a pressure image data set consisting of 32264 frames of images is first assembled to represent the 10 objects. Contrast enhancement algorithm was used to process the pressure image data set and combined with a convolutional neural network ResNet-PI to classify the 10 objects. For pressure images collected with the preestablished three experiment setups, an overall accuracy of 0.9854 is achieved. Compared with other systems based on Velostat sensor array, the system demonstrated in this study features improvements in structural robustness, detection repeatability and system reliability, suggesting its potential applications in emerging areas including human-computer interaction and smart health monitoring.

An Improved SRGAN Based Ambiguity Suppression Algorithm for SAR Ship Target Contrast Enhancement

Due to the specific characteristics of synthetic aperture radar (SAR), there will be ambiguity interference in SAR images, resulting in low contrast of the ship target to the clutter. This letter proposes an improved super-resolution generative adversarial network (ISRGAN) based ambiguity suppression algorithm for SAR ship target contrast enhancement. The proposed ISRGAN is the first attempt of using GAN for SAR ambiguity suppression. As a post-processing procedure, it does not need prior information of SAR systems, so it can be applied to various observation scenes and different acquisition modes. The generator of ISRGAN embeds the residual dense network (RDN) to optimally fuse the global and local features of the image, and it effectively improves the completeness of the feature information used for SAR ship target contrast enhancement. The superiority of ISRGAN on ambiguity suppression is validated on the Chinese Gaofen-3 imagery.

Hybridization of Divide-and-Conquer technique and Neural Network algorithm for better contrast enhancement in medical images

The aim of this work is to propose a new method for optimal contrast enhancement of a medical image. The main idea is to improve the Divide-and-Conquer method to enhance the contrast, and highlight the information and details of the image, based on a new conception of the Neural Network algorithm. The Divide-and-Conquer technique is a suitable method for contrast enhancement with an efficiency that directly depends on the choice of weights in the decomposition subspaces. A new hybrid algorithm was used for the optimal selection of weights, considering the optimization of the enhancement measure (EME). To evaluate the proposed model's effectiveness, experimental results were presented showing that the proposed hybrid technique is robustly effective and produces clear and high contrast images.

Edge-Aware Interactive Contrast Enhancement

Contrast enhancement is required in many applications. Many studies have been conducted to perform contrast enhancement automatically, but most of them do not consider various personal preferences for contrast. We propose an edge-aware interactive contrast enhancement algorithm to enable a user to adjust image contrast easily according to his or her preference. A user provides a parameter for controlling the global brightness and two types of scribbles to darken or brighten local regions in an image. Then, the proposed algorithm generates an edge-aware mask by propagating the scribbles to nearby regions and restores an enhanced image through a neural network, called e-IceNet. The user can provide annotations iteratively until he or she obtains a desired image. We train e-IceNet on guidance images to yield reliable results for diverse input images. We also propose two differentiable losses to train e-IceNet effectively and reliably. Extensive experiments demonstrate that the proposed e-IceNet is capable of allowing users to enhance images satisfactorily with simple scribbles, as well as producing enhanced images automatically.