A well-known issue of local (adaptive) histogram equalization (LHE) is over-enhancement (i.e., generation of spurious details) in homogenous areas of the image. In this paper, we show that the LHE problem has many solutions due to the ambiguity in ranking pixels with the same intensity. The LHE solution space can be searched for the images having the maximum PSNR or structural similarity (SSIM) with the input image. As compared to the results of the prior art, these solutions are more similar to the input image while offering the same local contrast.
Index Terms: histogram modification or specification, contrast enhancement
Histogram Equalization is a contrast enhancement technique in the image processing which uses the histogram of image. However histogram equalization is not the best method for contrast enhancement because the mean brightness of the output image is significantly different from the input image. There are several extensions of histogram equalization has been proposed to overcome the brightness preservation challenge. Contrast enhancement using brightness preserving bi-histogram equalization (BBHE) and Dualistic sub image histogram equalization (DSIHE) which divides the image histogram into two pa…
Indian Institute of Science Education and Research, Kolkata（印度科学教育与研究所，加尔各答）；Institute of Engineering & Management, Kolkata（工程与管理学院，加尔各答）；Indian Institute of Technology, Kanpur（印度理工学院，坎普尔）
In this paper, a comparative study between proposed hyper kurtosis based modified duo-histogram equalization (HKMDHE) algorithm and contrast limited adaptive histogram enhancement (CLAHE) has been presented for the implementation of contrast enhancement and brightness preservation of low contrast human brain CT scan images. In HKMDHE algorithm, contrast enhancement is done on the hyper-kurtosis based application. The results are very promising of proposed HKMDHE technique with improved PSNR values and lesser AMMBE values than CLAHE technique.
Histogram Equalization (HE) has been an essential addition to the Image Enhancement world. Enhancement techniques like Classical Histogram Equalization (CHE), Adaptive Histogram Equalization (ADHE), Bi-Histogram Equalization (BHE) and Recursive Mean Separate Histogram Equalization (RMSHE) methods enhance contrast, however, brightness is not well preserved with these methods, which gives an unpleasant look to the final image obtained. Thus, we introduce a novel technique Multi-Decomposition Histogram Equalization (MDHE) to eliminate the drawbacks of the earlier methods. In MDHE, we have decompo…
Central South University（中南大学）；University of Warwick（华威大学）
Image enhancement aims at processing an input image so that the visual content of the output image is more pleasing or more useful for certain applications. Although histogram equalization is widely used in image enhancement due to its simplicity and effectiveness, it changes the mean brightness of the enhanced image and introduces a high level of noise and distortion. To address these problems, this paper proposes image enhancement using fuzzy intensity measure and adaptive clipping histogram equalization (FIMHE). FIMHE uses fuzzy intensity measure to first segment the histogram of the origin…
National Institute of Technology Delhi（德里国家理工学院）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
Histogram Equalization (HE) is a popular method for contrast enhancement. Generally, mean brightness is not conserved in Histogram Equalization. Initially, Bi-Histogram Equalization (BBHE) was proposed to enhance contrast while maintaining a the mean brightness. However, when mean brightness is primary concern, Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE) is the best technique. There are several implementations of Histogram Equalization on FPGA, however to our knowledge MMBEBHE has not been implemented on FPGAs before. Therefore, we present an implementation of MMBEBHE on …
Enhancement of human vision to get an insight to information content is of vital importance. The traditional histogram equalization methods have been suffering from amplified contrast with the addition of artifacts and a surprising unnatural visibility of the processed images. In order to overcome these drawbacks, this paper proposes interative, mean, and multi-threshold selection criterion with plateau limits, which consist of histogram segmentation, clipping and transformation modules. The histogram partition consists of multiple thresholding processes that divide the histogram into two part…
Existing image enhancement methods fall short of expectations because with them it is difficult to improve global and local image contrast simultaneously. To address this problem, we propose a histogram equalization-based method that adapts to the data-dependent requirements of brightness enhancement and improves the visibility of details without losing the global contrast. This method incorporates the spatial information provided by image context in density estimation for discriminative histogram equalization. To minimize the adverse effect of non-uniform illumination, we propose defining spa…
MCKV Institute of Engineering（MCKV工程学院）；Jadavpur University（贾达普大学）
Automatic License Plate Recognition system is a challenging area of research now-a-days and binarization is an integral and most important part of it. In case of a real life scenario, most of existing methods fail to properly binarize the image of a vehicle in a congested road, captured through a CCD camera. In the current work we have applied histogram equalization technique over the complete image and also over different hierarchy of image partitioning. A novel scheme is formulated for giving the membership value to each pixel for each hierarchy of histogram equalization. Then the image is b…
An exact histogram specification (EHS) method modifies its input image to have a specified histogram. Applications of EHS include image (contrast) enhancement (e.g., by histogram equalization) and histogram watermarking. Performing EHS on an image, however, reduces its visual quality. Starting from the output of a generic EHS method, we maximize the structural similarity index (SSIM) between the original image (before EHS) and the result of EHS iteratively. Essential in this process is the computationally simple and accurate formula we derive for SSIM gradient. As it is based on gradient ascen…
Image processing algorithms are finding various applications in manufacturing and materials industries such as identification of cracks in the fabricated samples, calculating the geometrical properties of the given microstructure, presence of surface defects, etc. The present work deals with the application of Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm for improving the quality of the microstructure images of the Friction Stir Welded joints. The obtained results showed that the obtained value of quantitative metric features such as Entropy value and RMS Contrast value w…
Despite recent advances in image enhancement, it remains difficult for existing approaches to adaptively improve the brightness and contrast for both low-light and normal-light images. To solve this problem, we propose a novel 2D histogram equalization approach. It assumes intensity occurrence and co-occurrence to be dependent on each other and derives the distribution of intensity occurrence (1D histogram) by marginalizing over the distribution of intensity co-occurrence (2D histogram). This scheme improves global contrast more effectively and reduces noise amplification. The 2D histogram is …
In this paper an equalization technique for colour images is introduced. The method is based on nth root and nth power equalization approach but with optimization of the mean of the image in different colour channels such as RGB and HSI. The performance of the proposed method has been measured by the means of peak signal to noise ratio. The proposed algorithm has been compared with conventional histogram equalization and the visual and quantitative experimental results are showing that the proposed method over perform the histogram equalization.
Deep learning-based methods for low-light image enhancement typically require enormous paired training data, which are impractical to capture in real-world scenarios. Recently, unsupervised approaches have been explored to eliminate the reliance on paired training data. However, they perform erratically in diverse real-world scenarios due to the absence of priors. To address this issue, we propose an unsupervised low-light image enhancement method based on an effective prior termed histogram equalization prior (HEP). Our work is inspired by the interesting observation that the feature maps of …
Early detection and analysis of calcifications in mammogram images is crucial in a breast cancer diagnosis workflow. Management of calcifications that require immediate follow-up and further analyzing its benignancy or malignancy can result in a better prognosis. Recent studies have shown that deep learning-based algorithms can learn robust representations to analyze suspicious calcifications in mammography. In this work, we demonstrate that randomly equalizing the histograms of calcification patches as a data augmentation technique can significantly improve the classification performance for …
Bandırma Onyedi Eylül University（班德尔马九月十七大学）；Harran University（哈兰大学）
Contrast enhancement is very important in terms of assessing images in an objective way. Contrast enhancement is also significant for various algorithms including supervised and unsupervised algorithms for accurate classification of samples. Some contrast enhancement algorithms solve this problem by addressing the low contrast issue. Mean and variance based sub-image histogram equalization (MVSIHE) algorithm is one of these contrast enhancements methods proposed in the literature. It has different parameters which need to be tuned in order to achieve optimum results. With this motivation, in t…
Abstract PURPOSES this study aims to perform microcalsification detection by performing image enhancement in mammography image by using transformation of negative image and histogram equalization. image mammography with .pgm format changed to. jpg format then processed into negative image result then processed again using histogram equalization. the results of the image enhancement process using negative image techniques and equalization histograms are compared and validated with MSE and PSNR on each mammographic image. CONCLUSION: Image enhancement process on mammography image can be done, ho…
Indian Institute of Technology Guwahati（印度理工学院古瓦哈提分校）；Hanyang University（汉阳大学）
Image enhancement plays an important role in several application in the field of computer vision and image processing. Histogram specification (HS) is one of the most widely used techniques for contrast enhancement of an image, which requires an appropriate probability density function for the transformation. In this paper, we propose a fuzzy method to find a suitable PDF automatically for histogram specification using interval type - 2 (IT2) fuzzy approach, based on the fuzzy membership values obtained from the histogram of input image. The proposed algorithm works in 5 stages which includes …
Image enhancement aims at improving the information content of original image for a specific purpose. This purpose could be for visual interpretation or for effective extraction of required details. Nevertheless, some acquired images are often associated with pixels of low dynamic range and as such result in low contrast images. Enhancing the contrast therefore tends to increase the dynamic range of the gray levels in the acquired image so as to span the full intensity range. Techniques such as Histogram Equalization (HE) and fuzzy technique can be adopted for contrast enhancement. HE adjusts …
Communication University of China（中国传媒大学）；Beijing Institute of Graphic Communication（北京印刷学院）
In this paper, we propose a general framework to accelerate the universal histogram-based image contrast enhancement (CE) algorithms. Both spatial and gray-level selective down-sampling of digital images are adopted to decrease computational cost, while the visual quality of enhanced images is still preserved and without apparent degradation. Mapping function calibration is novelly proposed to reconstruct the pixel mapping on the gray levels missed by downsampling. As two case studies, accelerations of histogram equalization (HE) and the state-of-the-art global CE algorithm, i.e., spatial mutu…
This work proposes a method for underwater image enhancement using the principle of histogram equalization. Since underwater images have a global strong dominant colour, their colourfulness and contrast are often degraded. Before applying the histogram equalisation technique on the image, the image is converted from coloured image to a gray scale image for further operations. Histogram equalization is a technique for adjusting image intensities to enhance contrast. The colours of the image are retained using a convolutional neural network model which is trained by the datasets of underwater im…
Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）；National Institute of Technology Delhi（印度德里国家技术学院）
Background and Objective: Artificial intelligence (AI) methods coupled with biomedical analysis has a critical role during pandemics as it helps to release the overwhelming pressure from healthcare systems and physicians. As the ongoing COVID-19 crisis worsens in countries having dense populations and inadequate testing kits like Brazil and India, radiological imaging can act as an important diagnostic tool to accurately classify covid-19 patients and prescribe the necessary treatment in due time. With this motivation, we present our study based on deep learning architecture for detecting covi…
MRI (Magnetic Resonance Imaging) is a technique used to analyze and diagnose the problem defined by images like cancer or tumor in a brain. Physicians require good contrast images for better treatment purpose as it contains maximum information of the disease. MRI images are low contrast images which make diagnoses difficult; hence better localization of image pixels is required. Histogram Equalization techniques help to enhance the image so that it gives an improved visual quality and a well defined problem. The contrast and brightness is enhanced in such a way that it does not lose its origin…
M.S. University（M.S.大学）；S.T. Hindu College（S.T.印度教学院）
The performance of the Fingerprint recognition system will be more accurate with respect of enhancement for the fingerprint images. In this paper we develop a novel method for Fingerprint image contrast enhancement technique based on the discrete wavelet transform (DWT) and singular value decomposition (SVD) has been proposed. This technique is compared with conventional image equalization techniques such as standard general histogram equalization and local histogram equalization. An automatic histogram threshold approach based on a fuzziness measure is presented. Then, using an index of fuzzi…
There are several images that do not have uniform brightness which pose a challenging problem for image enhancement systems. As histogram equalization has been successfully used to correct for uniform brightness problems, a histogram equalization method that utilizes human visual system based thresholding(human vision thresholding) as well as logarithmic processing techniques were introduced later . But these methods are not good for preserving the local content of the image which is a major factor for various images like medical and aerial images. Therefore new method is proposed here. This m…
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
We present a baseline convolutional neural network (CNN) structure and image preprocessing methodology to improve facial expression recognition algorithm using CNN. To analyze the most efficient network structure, we investigated four network structures that are known to show good performance in facial expression recognition. Moreover, we also investigated the effect of input image preprocessing methods. Five types of data input (raw, histogram equalization, isotropic smoothing, diffusion-based normalization, difference of Gaussian) were tested, and the accuracy was compared. We trained 20 dif…
Comparative Analysis of Image Enhancement Techniques for Brain Tumor Segmentation: Contrast, Histogram, and Hybrid Approaches
AGH University of Krakow（克拉科夫AGH大学）；Universitas Pembangunan Nasional Veteran Yogyakarta（日惹国家发展老兵大学）；Hohai University（河海大学）；Universitas Ahmad Dahlan（艾哈迈德·达赫兰大学）
The appearance and structure of blood vessels in retinal images have an important role in diagnosis of diseases. This paper proposes a method for automatic retinal vessel segmentation. In this work, a novel preprocessing based on local histogram equalization is used to enhance the original image then pixels are classified as vessel and non-vessel using a classifier. For this classification, special feature vectors are organized based on responses to Morlet wavelet. Morlet wavelet is a continues transform which has the ability to filter existing noises after preprocessing. Bayesian classifier i…
Analysis of dental radiographs is an important part of the diagnostic process in daily clinical practice. Interpretation by an expert includes teeth detection and numbering. In this project, a novel solution based on adaptive histogram equalization and convolution neural network (CNN) is proposed, which automatically performs the task for dental x-rays. In order to improve the detection accuracy, we propose three pre-processing techniques to supplement the baseline CNN based on some prior domain knowledge. Firstly, image sharpening and median filtering are used to remove impulse noise, and the…
Image enhancement is a method of improving the quality of an image and contrast is a major aspect. Traditional methods of contrast enhancement like histogram equalization results in over/under enhancement of the image especially a lower resolution one. This paper aims at developing a new Fuzzy Inference System to enhance the contrast of the low resolution images overcoming the shortcomings of the traditional methods. Results obtained using both the approaches are compared.
Universitas Pembangunan Nasional Veteran Yogyakarta（雅加达退伍军人国家发展大学）
Image processing can be applied in the detection of egg embryos. The egg embryos detection is processed using a segmentation process. The segmentation divides the image according to the area that is divided. This process requires improvement of the image that is processed to obtain optimal results. This study will analyze the detection of egg embryos based on image processing with image enhancement and the concept of segmentation using the watershed method. Image enhancement in preprocessing in image improvement uses a combination of Contrast Limited Adaptive Histogram Equalization (CLAHE) and…
Luminosity-Adaptive Contrast Enhancement Using CLAHE for Retinal Fundus Images with Quantitative Validation and Comparative Analysis
Independent Researcher, Department of Information and Computer Engineering, Scottsdale, AZ, USA（独立研究者，信息与计算机工程系，斯科茨代尔，亚利桑那州，美国）；Seidenberg School of Computer Science and Information Systems, Pace University, New York City, NY, USA（塞登堡计算机科学与信息系统学院，帕克大学，纽约市，纽约州，美国）
Fabric defect detection is a crucial quality control step in the textile manufacturing industry. In this article, machine vision system based on the Sylvester Matrix Based Similarity Method (SMBSM) is proposed to automate the defect detection process. The algorithm involves six phases, namely resolution matching, image enhancement using Histogram Specification and Median-Mean Based Sub-Image-Clipped Histogram Equalization, image registration through alignment and hysteresis process, image subtraction, edge detection, and fault detection by means of the rank of the Sylvester matrix. The experim…
No-reference image quality assessment (NR-IQA) aims to measure the image quality without reference image. However, contrast distortion has been overlooked in the current research of NR-IQA. In this paper, we propose a very simple but effective metric for predicting quality of contrast-altered images based on the fact that a high-contrast image is often more similar to its contrast enhanced image. Specifically, we first generate an enhanced image through histogram equalization. We then calculate the similarity of the original image and the enhanced one by using structural-similarity index (SSIM…
The University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；Texas State University（德克萨斯州立大学）
This research presents an improved real-time face recognition system at a low resolution of 15 pixels with pose and emotion and resolution variations. We have designed our datasets named LRD200 and LRD100, which have been used for training and classification. The face detection part uses the Viola-Jones algorithm, and the face recognition part receives the face image from the face detection part to process it using the Local Binary Pattern Histogram (LBPH) algorithm with preprocessing using contrast limited adaptive histogram equalization (CLAHE) and face alignment. The face database in this s…
In this paper we use a logarithmic model for gray level image enhancement. We begin with a short presentation of the model and then, we propose a new formula for the mean dynamic range. After that we present two image transforms: one performs an optimal enhancement of the mean dynamic range using the logarithmic addition, and the other does the same for positive and negative values using the logarithmic scalar multiplication. We present the comparison of the results obtained by dynamic ranges optimization with the results obtained using classical image enhancement methods like gamma correction…
Chandigarh Engineering College（昌迪加尔工程学院）；Thapar Institute of Engineering & Technology, University（塔帕尔工程技术大学）
Recent innovations shows that blending of details captured by single Low Dynamic Range (LDR) sensor overcomes the limitations of standard digital cameras to capture details from high dynamic range scene. We present a method to produce well-exposed fused image that can be displayed directly on conventional display devices. The ambition is to preserve details in poorly illuminated and brightly illuminated regions. Proposed approach does not require true radiance reconstruction and tone manipulation steps. The aforesaid objective is achieved by taking into account local information measure that s…
Many image segmentation techniques have been developed over the past two decades for segmenting the images, which help for object recognition, occlusion boundary estimation within motion or stereo systems, image compression, image editing. In this, there is a combined approach for segmenting the image. By using histogram equalization to the input image, from which it gives contrast enhancement output image .After that by applying median filtering,which will remove noise from contrast output image . At last I applied fuzzy c-mean clustering algorithm to denoising output image, which give segmen…
Indira Gandhi Institute of Technology, GGSIP University（英迪拉·甘地工程技术学院，GGSIP大学）
Evolutionary algorithms are metaheuristic techniques that derive inspiration from the natural process of evolution. They can efficiently solve (generate acceptable quality of solution in reasonable time) complex optimization (NP-Hard) problems. In this paper, automatic image enhancement is considered as an optimization problem and three evolutionary algorithms (Genetic Algorithm, Differential Evolution and Self Organizing Migration Algorithm) are employed to search for an optimum solution. They are used to find an optimum parameter set for an image enhancement transfer function. The aim is to …
Microalgae counting is used to measure biomass quantity. Usually, it is performed in a manual way using a Neubauer chamber and expert criterion, with the risk of a high error rate. This paper addresses the methodology for automatic identification of Scenedesmus microalgae (used in the methane production and food industry) and applies it to images captured by a digital microscope. The use of contrast adaptive histogram equalization for pre-processing, and active contours for segmentation are presented. The calculation of statistical features (Histogram of Oriented Gradients, Hu and Zernike mome…
University of Dhaka（达卡大学）；Qatar University（卡塔尔大学）；Qatar Foundation（卡塔尔基金会）；Universiti Kebangsaan Malaysia（马来西亚国民大学）；College of Medicine（医学院）；University of Engineering and Technology, Peshawar（白沙瓦工程技术大学）
The use of computer-aided diagnosis in the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the medical infrastructure. Chest X-ray (CXR) imaging has several advantages over other imaging techniques as it is cheap, easily accessible, fast and portable. This paper explores the effect of various popular image enhancement techniques and states the effect of each of them on the detection performance. We have compiled the largest X-ray dataset called COVQU-20, consisting of 18,479 normal…
To extract liver from medical images is a challenging task due to similar intensity values of liver with adjacent organs, various contrast levels, various noise associated with medical images and irregular shape of liver. To address these issues, it is important to preprocess the medical images, i.e., computerized tomography (CT) and magnetic resonance imaging (MRI) data prior to liver analysis and quantification. This paper investigates the impact of permutation of various preprocessing techniques for CT images, on the automated liver segmentation using deep learning, i.e., U-Net architecture…
Image enhancement is an important image processing technique that processes images suitably for a specific application e.g. image editing. The conventional solutions of image enhancement are grouped into two categories which are spatial domain processing method and transform domain processing method such as contrast manipulation, histogram equalization, homomorphic filtering. This paper proposes a new image enhance method based on dictionary learning. Particularly, the proposed method adjusts the image by manipulating the rarity of dictionary atoms. Firstly, learn the dictionary through sparse…
University of Moratuwa（莫勒图沃大学）；Base Hospital Kebithigollawa（科比蒂戈勒沃基地医院）
The morphology of blood vessels in retinal fundus images is an important indicator of diseases like glaucoma, hypertension and diabetic retinopathy. The accuracy of retinal blood vessels segmentation affects the quality of retinal image analysis which is used in diagnosis methods in modern ophthalmology. Contrast enhancement is one of the crucial steps in any of retinal blood vessel segmentation approaches. The reliability of the segmentation depends on the consistency of the contrast over the image. This paper presents an assessment of the suitability of a recently invented spatially adaptive…
University of Moratuwa（莫勒图沃大学）；Rajarata University of Sri Lanka（斯里兰卡拉贾拉塔大学）；Base Hospital Kebithigollawa（克比蒂戈勒沃基地医院）
This paper presents a super-efficient spatially adaptive contrast enhancement algorithm for enhancing infrared (IR) radiation based superficial vein images in real-time. The super-efficiency permits the algorithm to run in consumer-grade handheld devices, which ultimately reduces the cost of vein imaging equipment. The proposed method utilizes the response from the low-frequency range of the IR image signal to adjust the boundaries of the reference dynamic range in a linear contrast stretching process with a tunable contrast enhancement parameter, as opposed to traditional approaches which use…
This report describes the experimental analysis of proposed underwater image enhancement algorithms based on partial differential equations (PDEs). The algorithms perform simultaneous smoothing and enhancement due to the combination of both processes within the PDE-formulation. The framework enables the incorporation of suitable colour and contrast enhancement algorithms within one unified functional. Additional modification of the formulation includes the combination of the popular Contrast Limited Adaptive Histogram Equalization (CLAHE) with the proposed approach. This modification enables t…
Fast Dust Sand Image Enhancement Based on Color Correction and New Membership Function
Informatics Institute for Postgraduate Studies, Iraqi Commission for Computer and Informatics（伊拉克计算机与信息委员会研究生信息学研究所）；Al-Khwarizmi College of Engineering, University of Baghdad（巴格达大学阿尔-花剌子密工程学院）
