Showing papers on "Histogram equalization published in 2023"

Underwater Image Enhancement Method via Multi-Interval Subhistogram Perspective Equalization

Abstract: Due to the selective attenuation of light in water, captured underwater images exhibit poor visibility and cause considerable challenges for vision tasks. The structural and statistical properties of different regions of degraded underwater images are damaged at different levels, resulting in a global nonuniform drift of the feature representation, causing further degradation of visual performance. To handle these issues, we present an underwater image enhancement method via multi-interval subhistogram perspective equalization to address the issues posed by underwater images. We estimate the degree of feature drifts in each area of an image by extracting the statistical characteristics of the image, using this information to guide feature enhancement to achieve adaptive feature enhancement, thereby improving the visual effect of degraded images. We first design a variational model that uses the difference between data items and regular items to improve the color correction performance of the method based on subinterval linear transformation. In addition, a multithreshold selection method, which adaptively selects a threshold array for interval division, is developed. Ultimately, a multi-interval subhistogram equalization method, which performs histogram equalization in each subhistogram to improve the image contrast, is presented. Experiments on underwater images with various scenarios demonstrate that our method significantly outperforms many state-of-the-art methods qualitatively and quantitatively.

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

Abstract: Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. Finally, the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching (HM), Histogram Equalization (HE), fuzzy technique, fuzzy type Π, and Contrast Limited Histogram Equalization (CLAHE). The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement quality. Different realistic datasets of different modalities and diseases are tested and implemented. Also, real datasets are tested in the simulation analysis.

Performance Analysis of Graph theory-based Contrast Limited Adaptive Histogram Equalization for Image Enhancement

Abstract: Nowadays, image enhancement has become a major area of research because of the development of applications that are based on vision.Several digital image processing systems employ such image enhancement strategies with the help of graph theory. As the visibility level in low contrast image features is very less,several image enhancement strategies have been introduced with spatial transformations to enhance image qualityfor improved visualization. Nowadays, image processing plays an important role in the analysis of a patient’s health status and has become extremely popular in medical areas for a wide range of clinical assessments. Generally, medical images contain several complex areas and thereby,few pre-processing approaches are applied to reduce the challenges that occur during different phases of the CAD system. Furthermore, because of external noise interferences, poor illuminating settings as well as other imaging device limitations, the clinical diagnosis becomes a challenging process and medical images do not provide important information for precise categorization. Medical images are available in a variety of applications such as computed tomography, Magnetic Resonance Imaging (MRI), mammography, chest X-ray (CXR), and many more. Only the pixel intensity variations between different areas as well as object boundary information are essential for categorization and must be enhanced simultaneously. As a result, the rate of classification in medical images and intensity are increased so that every object during the analysis can be easily identified. The main goal of any image enhancement process is to enhance the quality of the image by reducing noise and on other hand by using three different algorithms such as Luminance Modulation (LM), Gradient Modulation (GM), and Dynamic Histogram Equalization (DHE). These three algorithms are designed with the help of graph theory for effective preservation of edges, losses, and efficient smoothing and to preserve the basic information without any modifications. Image restoration is also referred to as image enhancement and it is concerned with the precise assessment of real images. Generally, the degradation process is not included in many of the image-enhancement approaches that are already existing. Furthermore, with the application of enhancement techniques, the degradation process for medical images results in some significant performance loss. Several techniques have been proposed and the technique which is examined in this research is image enhancement that is based on histogram which mainly concentrates on equalizing the histogram of values. Histogram Equalization (HE) possesses a few basic properties such as altering spatial patterns as well as intensity which in turn results in significant challenges in medical imaging. As a result,Contrast Limited Adaptive Histogram Equalization (CLAHE) is proposed in this work as a feasible approach for medical image analysis to address the problem. The suggested research work demonstrates that the intensity limiting image enhancement with histogram equalization detects the irregularities in dense mammograms with enhanced quality.

Local-Ternary-Pattern-Based Associated Histogram Equalization Technique for Cervical Cancer Detection

Abstract: Every year, cervical cancer is a leading cause of mortality in women all over the world. This cancer can be cured if it is detected early and patients are treated promptly. This study proposes a new strategy for the detection of cervical cancer using cervigram pictures. The associated histogram equalization (AHE) technique is used to improve the edges of the cervical image, and then the finite ridgelet transform is used to generate a multi-resolution picture. Then, from this converted multi-resolution cervical picture, features such as ridgelets, gray-level run-length matrices, moment invariant, and enhanced local ternary pattern are retrieved. A feed-forward backward propagation neural network is used to train and test these extracted features in order to classify the cervical images as normal or abnormal. To detect and segment cancer regions, morphological procedures are applied to the abnormal cervical images. The cervical cancer detection system’s performance metrics include 98.11% sensitivity, 98.97% specificity, 99.19% accuracy, a PPV of 98.88%, an NPV of 91.91%, an LPR of 141.02%, an LNR of 0.0836, 98.13% precision, 97.15% FPs, and 90.89% FNs. The simulation outcomes show that the proposed method is better at detecting and segmenting cervical cancer than the traditional methods.

Multiscale Fusion Algorithm for Underwater Image Enhancement Based on Color Preservation

Abstract: Underwater images regularly exhibit color deviation and low contrast attributed to attenuation and scattering associated with wavelength and distance. For solving these two degradation problems, we propose an efficient underwater image enhancement model, which takes a multiscale fusion strategy as a pivot to concurrently achieve color balance and contrast enhancement. Specifically, we target two core degradation problems through two preprocessing steps. On the one hand, the white balance strategy enhances the appearance of the image by suppressing unnecessary colors to solve the color deviation problem. On the other hand, we employ a color-preserving adaptive histogram equalization (CP-AHE) to obtain an image with higher contrast by jointly processing the three color channels. In the fusion phase, a fusion strategy based on the detail-preserving decomposition of the edge-protected structural blocks is used to converge the white balance and CP-AHE inputs. Extensive experiments on common underwater image datasets have shown the advantages of the proposed method over other state-of-the-art underwater image methods in terms of visual and quantitative evaluation.

Improved Model for Genetic Algorithm-Based Accurate Lung Cancer Segmentation and Classification

Abstract: Lung Cancer is one of the hazardous diseases that have to be detected in earlier stages for providing better treatment and clinical support to patients. For lung cancer diagnosis, the computed tomography (CT) scan images are to be processed with image processing techniques and effective classification process is required for appropriate cancer diagnosis. In present scenario of medical data processing, the cancer detection process is very time consuming and exactitude. For that, this paper develops an improved model for lung cancer segmentation and classification using genetic algorithm. In the model, the input CT images are pre-processed with the filters called adaptive median filter and average filter. The filtered images are enhanced with histogram equalization and the ROI(Regions of Interest) cancer tissues are segmented using Guaranteed Convergence Particle Swarm Optimization technique. For classification of images, Probabilistic Neural Networks (PNN) based classification is used. The experimentation is carried out by simulating the model in MATLAB, with the input CT lung images LIDC-IDRI (Lung Image Database Consortium-Image Database Resource Initiative) benchmark Dataset. The results ensure that the proposed model outperforms existing methods with accurate classification results with minimal processing time.

Chest Disease Classification Using Convolutional Neural Networks

Abstract: Chest diseases pose major health risks to people globally. Early diagnosis of these conditions enables early treatment, which can prevent death. The healthcare system benefits greatly from the use of Convolutional Neural Networks (CNN), particularly when it comes to early disease prediction. X- rays serve as one of the key factors that accurately classify disorders of the chest. The prediction of chest diseases such Atelectasis, Cardiomegaly, Lung Consolidation, Edema, Pleural Thickening and Pneumothorax from X-ray images is the objective of this project. The CNN Model is used to analyze the disease classification and the results are explained in terms of accuracy. Preprocessing with images can enhance the model’s accuracy. For that, we used some image preprocessing techniques which include Histogram Equalization, Bilateral Filter, Gaussian Blur and Contrast Limited Adaptive Histogram Equalization. These techniques were used to remove the unwanted noise from the X ray images and improve luminance of the images which leads to produce more accurate decisions. The dataset consists of 1 csv file and an X-ray image folder that contains six classes of disease and 1,120 X-rays. Convolutional neural networks (CNNs) are described in the research as a tool for diagnosing disorders of the chest. The architecture of CNN is presented, as well as its guiding principles. Among those preprocessing techniques, Contrast Limited Adaptive Histogram Equalization technique gave more accuracy which is nearly 91.2 %. Results that compare accuracy and network training time are shown.

Two-Stage Decolorization Based on Histogram Equalization and Local Variance Maximization

Abstract: Image decolorization is widely used in single-channel image processing, black-and-white printing, etc. Decolorization aims to generate a perceptually satisfactory gray image that preserves the contrast of the color image. It is known that histogram equalization can enhance the global image contrast by effectively spreading out the most frequent intensity values. Meanwhile, local contrast features such as salient edges and local details have large local variances, which can be enhanced by maximizing local variance. Inspired by these facts, we propose a two-stage decolorization method based on histogram equalization and local variance maximization. In the first stage, we assume that the decolorized gray image is a linear combination of the three channels of the color image, and the combination coefficients are three global weights. Then we propose a constrained variational histogram equalization model to optimize the global weights. The resulting gray image has good global contrast. To further enhance the local contrast, in the second stage, we use local weight combination to express the color image and maximize the local variance by forcing the local weights to be close to the global weights. Numerically, the global weights can be estimated by a gradient-based solver or a discrete searching solver, and the local weights are solved by an iterative solver. Theoretically, we discuss the properties of the energy functions and the convergence of the algorithm. Our proposed method better preserves global and local contrast than state-of-the-art decolorization algorithms.

Showthrough and Strikethrough print defect detection using histogram equalization based computer vision method

Abstract: This paper presents a comparatively simple approach for showthrough and strikethrough print defect detection using computer vision method. Showthrough and strikethrough are common printing problem and are typically functions of a paper’s opacity. Under normal lighting condition the visibility of printing on the reverse side of printed paper is termed as showthrough whereas the penetration of ink to the other side is termed as strikethrough. Moreover the intensity of showthrough pixel is extremely low thus it is difficult to identify the showthrough pixel from the printed area. On the other hand strikethrough is the result of penetration of ink through paper and depends on the absorbent nature of paper. Comparatively the intensity of the strikethrough pixel is higher than that of the showthrough but due to similar intensity of the ink of the printed pixel and strikethrough pixel, both overlapped with each other in the foreground of the image. These print defects can degrade the image quality as well as print production. In this study, the detection of these two print defects achieved using histogram equalization technique, to enhance the contrast between foreground and back ground pixels. A global thresholding algorithm was applied on a histogram equalized image to segment the printed area from the background of the image. Pixels in the background which are considered as showthrough and strike through pixels are identified by image subtraction. The pictorial representations of the results show the remarkable potential of the proposed technique which can be possible alternative of present subjective measures of showthrough and strikethrough.

Gamma Correction for Brightness Preservation in Natural Images

Abstract: Due to improper acquisition settings and other noise artifacts, the image degraded to yield poor mean preservation in brightness. The simplest way to improve the preservation is the implementation of histogram equalization. Because of over-enhancement, it failed to preserve the mean brightness and produce the poor quality of the image. This paper proposes a multi-scale decomposition for brightness preservation using gamma correction. After transformation to hue, saturation and intensity (HSI) channel, the 2D- discrete wavelet transform decomposed the intensity component into low and high-pass coefficients. At the next phase, gamma correction is used by auto-tuning the scale value. The scale is the modified constant value used in the logarithmic function. Further, the scale value is optimized to obtain better visual quality in the image. The optimized value is the weighted distribution of standard deviation-mean of low pass coefficients. Finally, the experimental result is estimated in terms of quality assessment measures used as absolute mean brightness error, the measure of information detail, signal to noise ratio and patch-based contrast quality in the image. By comparison, the proposed method proved to be suitably remarkable in retaining the mean brightness and better visual quality of the image.

De-noising the image using DBST-LCM-CLAHE: A deep learning approach

Abstract: Abstract Histogram Equalization (HE) is one of the most popular techniques for this purpose. Most histogram equalization techniques, including Contrast Limited Adaptive Histogram Equalization (CLAHE) and Local Contrast Modification CLAHE (LCM CLAHE), use a fixed block size technique for feature enhancement. Due to this, all these state of art techniques are used to give poor denoising performance after feature enhancement. In this paper, a deep learning based new approach, namely Dynamic Block Size Technique (DBST), is used to improve image denoising. In this approach, we use the Categorical Subjective Image Quality (CSIQ) image set, an image database generally used for preprocessing of images. The results obtained from experiments show better performance for different important parameters (used by state of art techniques). The work is novel in the preprocessing of images because in this work, we classify the image depending upon the image features for selecting appropriate block sizes dynamically during preprocessing. Proposed work outperforms in terms of PSNR, MSE, NRMSE, SSIM and SYNTROPY. The average respective values are 18.92, 863.86, 0.25, 0.81 and 19.35 and are better in comparison of CLAHE and LCM CLAHE.

Analysis and Comparison of Image Enhancement Technique for Improving PSNR of Lung Images by Unsharp Mask Filtering Technique over Histogram Equalization Technique

Abstract: Aim: The goal of study in this image enhancement technique is to enhance both contrast and sharpness of an image simultaneously to improve PSNR. Materials and Methods: Both unsharp mask filter and novel histogram equalization techniques were implemented on lung images which were collected from kaggle software. Samples were considered as (N=30) for unsharp mask filtering 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 as 60. Matlab coding was written for extracting PSNR values of each image. Comparison and analysis has been made through SPSS software. Results: In the final output of image enhancement, novel histogram equalization technique shows better performance in improving PSNR of lung images than unsharp mask filtering technique. Comparison of PSNR values are done by independent sample test using IBM-SPSS software. There is a statistical difference between histogram technique and unsharp mask filtering. The novel histogram equalization technique showed higher results of PSNR (67.2860dB) with (p=0.04) in comparison with unsharp mask filtering (37.9313dB). Conclusion: Within this research study histogram equalization image enhancement technique has greater PSNR value of lung images than unsharp mask filtering technique.

Image Recognition and Enhancement Using Multi-Scale Retinex and Histogram Equalization

Abstract: Nowadays, the need for a safe and at ease device is favoured by using every character in society. A fee-effective system is needed for Aerial surveillance systems capable of enhancing situational focus in the course of a couple of scales of place and time. With the assistance of an aerial surveillance machine, we are capable of accumulating a device that could report unmanned vehicles and humans. With improvements in technology, Unmanned motors and optical sensors are being fast produced with powerful usage of resources, therefore, it has ended up being less complicated to get images from the UAV at a low price. But the selection of those pixy received is reduced because of climatic conditions like fog, rain, harsh sunlight, etc. So, we advocate a photograph processing technique as a manner to use algorithms that include single-scale retinex, Multiscale retinex, histogram equalization, and colour recuperation to grow the fine of photographs. These select images have a higher evaluation, and subsequently can be used for better item detection. 1. The program can determine the objects without getting observed.2. The program can find multiple items. Three. MSRP for similar processing like histogram equalization and item detection to get the exceptional possible output

Quality Analysis and Improvement of 3D Panoramic Stitching Sequence Image

Abstract: Using a computer to splice images with overlapping parts is the current mainstream method for 3D panorama construction. However, the quality of the spliced sequence images cannot be guaranteed due to the uneven quality of the resulting panorama. Aiming at quality problems such as inconsistent brightness and contrast of stitched sequence images and easy noise generation during the shooting process, a batch quality improvement process for multiple sequence images required for 3D panorama construction is proposed, which is different from single image processing; firstly, the median value is used The filtering method is used to denoise multiple sequential images, secondly to make the gray range of multiple images consistent, and finally to improve the contrast of the sequential images by using histogram equalization. Experimental results show that this method has a good effect on improving the quality of 3D panorama stitching sequence images.

An FPGA-based real-time infrared target detection system with visual image positioning

Abstract: This paper builds a real-time infrared target detection system with a visual positioning function based on FPGA, a visual camera, and an infrared camera. Firstly, the whole system is built based on FPGA. The design and implementation of the infrared image acquisition module, a data storage module, SD card module, and image display module are completed. Ping pong operation is used for the data storage modules to realize video stream transmission. The experiment results show that the system built in this paper can collect and display the target in real-time. To improve the system performance, several image processing algorithms are proposed, including an improved median filtering algorithm, linear transformation, and Laplacian sharpening algorithm; a combined algorithm of histogram equalization, Gamma transform, and Laplacian sharpening; a target detection algorithm combined with threshold segmentation and a background difference algorithm; and a visual localization algorithm. Software simulation and FPGA hardware implementation results show the effectiveness of the proposed algorithms.

Automated Pneumonia Detection using deep features in chest X-ray images

Abstract: Pneumonia is swelling of the lungs that is usually caused by an infection. This disease is considered as one of the most common reasons for US children to be hospitalized. According to American Thoracic Society (ATS), the cost of treating pneumonia cases in hospitals reached 9.5 billion dollar. The appropriate treatment and recovery process for this disease are linked to early diagnosis. In this work a novel method is proposed for detecting the pneumonia and help the radiologists in their decision making process. First, histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) are calculated for chest X-ray images. Then, the images extracted are fed to a model consisting of two stream of Convolutional Neural Networks (CNN) that was trained on the Pneumonia Kermany dataset. Finally, several machine learning classifiers are employed to perform the detection process based on the deep features extracted. The proposed system achieves 97.86% in terms of accuracy on the Kermany dataset, which is satisfactory when compared to recently published works.

AI-Based Image Processing for Photoresist Latent Image Enhancement : AM: Advanced Metrology

Abstract: A new image processing and AI based methodology is described that accurately locates alignment marks in the latent images of an exposed wafer before post exposure bake (PEB) despite minimal contrast. The detected marks will be used for the alignment of low-contrast PEB images and allow multiple exposures without the need to develop the photoresist or PEB for each exposure. The critical steps in the algorithm are to remove high contrast elements from the image and perform adaptive histogram equalization followed by template matching to determine the location of the marks in units of pixels. In 98% of the test cases, the algorithm correctly located the alignment marks; processing an image requires 3.5 sec on a consumer laptop.

Рентген суреттерінің контрастын гамма-түзету негізіндеартты

Abstract: Due to the nonlinearity of the brightness function created by many medical registration devices, the quality of the medical image deteriorates, which creates problems in the visual research work of doctors. As an example, we can take X-rays. The article studies methods of increasing the contrast of graphic images, in particular methods of improving the Абай атындағы ҚазҰПУ-нің ХАБАРШЫСЫ, «Физика-математика ғылымдары» сериясы, No1(77), 2022133quality of X-ray images. The research was carried out in several stages. Attempts were made to increase the contrast of several dozen X-ray images in order to select the best image brightness using the IMADJUST function of the MATLAB system. During the experiments, an increase in contrast was observed, as a result of which a brightness range corresponding to a visual improvement in contrastwas selected. The choice of variables γ for the selected range of brightness of the image is made. To obtain the best result, the method of equalizing the histogram of the X-ray image was first used. To quantify the results of the transformation, the niqeand brisque evaluation functions are used, which do not use reference images. Көптеген медициналық тіркеу құрылғылары жасаған жарықтылық функциясының сызықты емес болуынан медициналық бейненің сапасы нашарлап, дәрігерлердің визуалды зерттеу жұмысында қиындықтар туғызады. Мысал ретінде рентген суреттерін алуға болады. Мақалада графикалық бейнелердің контрастын арттыру, дербес жағдайда рентген суреттерінің сапасын жақсарту әдістері зерттелген. Зерттеулер бірнеше кезеңде жүргізілді. MATLAB жүйесінің Imadjust функциясы арқылы ең жақсы кескін жарықтығын таңдау мақсатында бірнеше ондаған рентген бейнелерінің контрастын арттыру әрекеттері орындалды. Тәжірибелер орындау кезінде контрасттың жоғарылауы байқалды, нәтижесінде контрасттың визуалды жақсаруына сәйкес болатын жарықтылық диапазоны таңдалды. Таңдалған кескін жарықтығының диапазоны үшінγ айнымалы мәндерін таңдау орындалды. Жақсырақ нәтиже алу үшін алдымен рентген суретінің гистограммасын туралау әдісі қолданылды. Түрлендіру нәтижелерін сандық бағалау үшін эталондық кескіндерді пайдаланбайтын niqe және brisque бағалау функциялары қолданылады. Из-за нелинейности функции яркости, создаваемой многими медицинскими регистрационными устройствами, качество медицинского изображения ухудшается, что создает проблемы в визуальной исследовательской работе врачей. В качестве примера можно взять рентгеновские снимки. В статье изучены методы повышения контрастности графических изображений, в частности методы улучшения качества рентгеновских снимков. Исследования проводились в несколько этапов. Были предприняты попытки увеличить контрастность нескольких десятков рентгеновских изображений с целью выбора наилучшей яркости изображения с помощью функции IMADJUST системы MATLAB. При выполнении опытов наблюдалось увеличение контрастности, в результате чего был выбран диапазон яркости, соответствующий визуальному улучшению контраста. Выполнен выбор переменных γ для выбранного диапазона яркости изображения. Для получения лучшего результата сначала был использован метод выравнивания гистограммы рентгеновского снимка. Для количественной оценки результатов преобразования используются функции оценки niqe и brisque, которые не используют эталонные изображения.

Color Compensation Based Fusion for Enhancement of Weakly Artitifical Illuminated Indoor Images

Abstract: In this article, a new approach is introduced to enhance the weakly artificial illuminated indoor images by blending three color compensated versions obtained by Color Equalization (CE), Color Mixing (CM) and Gray-World (GW) methods. This method ignores the disadvantages of blue dominancy, green dominancy and black artefacts results respectively due to CE, GW and CM and this forms prior for this fusion approach. To fuse the advantages of all three enhanced versions of low illuminated images, the multi-scale image fusion technique is employed. To get the information of background scenes, contrast limited adaptive histogram equalization (CLAHE) is applied to the blended image. The experimental results illustrate that the approach proposed in this work outperforms the existing methods.

Underwater image re-enhancement with blend of Simplest Colour Balance and Contrast Limited Adaptive Histogram Equalization Algorithm

Abstract: A trial has been made for a technique for the enhancement and restoration of underwater images. To resolve this a fusion algorithm that consists of a simple color balance algorithm and Contrast Limited Adaptive Histogram Equalization (CLAHE) contrast enhancements algorithm is carried out. To increase the effect of R, G, and B values in the image’s color spectrum, we have used a simple color balance algorithm. This simple color balance algorithm observes the image’s brightest and darkest values and stretches as much as it can between maximum and minimum [0,255]. To increase the depths in the images we perform a contrast enhancement. The projected underwater image processing output is contrasted with a number of other current models. A comparison of our model shows an advantage over others in some results

Research on Histogram Equalization Algorithm Based on Optimized Adaptive Quadruple Segmentation and Cropping of Underwater Image(AQSCHE)

Abstract: Due to the uncertain, diverse, and light-attenuating characteristics of the underwater environment, underwater images have low contrast and unclear problems. This paper proposes a histogram equalization algorithm based on optimized adaptive image quadruple segmentation and cropping (AQSCHE). Compared with the traditional histogram equalization underwater image enhancement algorithm, this algorithm introduces histogram quadruple segmentation and cropping technology. Using the exposure value and segmentation point calculation formula that optimizes the distribution range of the histogram, perform quadruple segmentation on the image to obtain a more refined histogram. The adaptive histogram clipping is realized by constructing the clipping parameter z to adjust the contrast and brightness of the image. The original image is enhanced by the equalization of the sub-histogram and the histogram of each channel. Finally, the simulation experiments verify the enhancement effect of the proposed algorithm AQSCHE on underwater images. The processed underwater image has higher contrast, clearer and more natural in subjective evaluation, and has better visual effect; in the image objective evaluation indicators, information entropy (Entropy), peak signal to noise ratio (PSNR), structural similarity index (SSIM) and universal color image quality evaluator (UCIQE), etc., this algorithm also outperforms other common algorithms such as HE and CLAHE.

A saturation-value histogram equalization model for color image enhancement

Abstract: In this paper, we propose and develop a novel variational histogram equalization framework for color image enhancement. The main idea is to propose a variational model containing an energy functional which leads to adjust the pixel values of an input image so that the resulting histogram of saturation and value components can be redistributed uniformly. In the proposed model, we make use of a mean brightness constraint term to guarantee the preservation of mean brightness information. The saturation-value total variation is incorporated for color correction and noise elimination in order to improve the image quality. Theoretically, the existence of the minimizer of the variational model and the convergence of the proposed algorithm are analyzed. Experimental results are shown to demonstrate the feasibility and effectiveness of the proposed model.