Histogram Equalization Method Research Articles

Optimized multi-scale framework for image enhancement using spatial information-based histogram equalization

ABSTRACT The histogram equalization method, used to improve images, minimizes the amount of pixel intensities, which results in the loss of detail and an unnatural appearance. This study presents an approach for enhancing low contrast images based on their inherent characteristics. The statistical parameter skewness is derived from the photographs to facilitate the classification process into dark and bright images. Based on the classification, appropriate dark and bright pass filters are applied on the multi-level decomposed images to extract the significant edge details. The level of decomposition is optimized using particle swarm optimization. The extracted edge details are utilized by the two-dimensional histogram equalization technique. It leverages the combined presence of edge information and pixel intensities in the low contrast image. The algorithm's efficacy is assessed on three databases, namely CCID, LOL, and DRESDEN, through the utilization of standard deviation (SD), contrast improvement index (CII), discrete entropy (DE), the natural image quality evaluator (NIQE), and Kullback–Leibler distance (KL). Based on the empirical findings, it can be observed that the suggested methodology exhibits better performance compared to alternative methods, including deep learning architectures, in terms of high CII, SD, DE, and low NIQE, KL values.

Improving Digital Image Clarity: A Study on the Application of Histogram Equalization for Noise Correction

This study aims to improve the clarity of digital images by examining the application of the histogram equalization method for noise correction. Noise in digital images is often a major challenge in maintaining the clarity and authenticity of visual information. Histogram equalization has been recognized as an effective method in improving image contrast and reducing the effects of noise. In this research, we conducted experiments by applying histogram equalization techniques to various types of digital images that are affected by noise. We analyzed the results by comparing the clarity and quality of the images before and after applying this method. The results of this research show that histogram equalization is able to significantly improve the clarity of digital images by reducing the effects of noise without sacrificing important details in the image. The implication of this discovery is the potential use of the histogram equalization method as an effective tool in improving the quality of digital images that are affected by noise.

Lung computed tomography image enhancement using U‐Net segmentation

AbstractThe goal of image enhancement methods is to improve image's quality. The efficacy of U‐net is evident through its extensive utilization across various significant image modalities, involving computed tomography (CT) scans, magnetic resonance imaging, X‐rays, and microscopy. In this study, we provided a novel and efficient strategy to improve lung CT images based on segmentation using U‐Net architecture. Subsequently, contrast enhancement was performed using adaptive histogram equalization and dark channel prior methods. Finally, the lightness of the lung CT image was enhanced using nonlinear mapping. The contrast enhancement performance of the suggested method is quantified by various measures like the average gradient, mean of the local standard deviation, contrast enhancement measure, and structural similarity index. The performance of the suggested method is compared against other methods, and the results indicate that the suggested method achieves better quality measures of 23.4907, 55.20341, 0.961674, and 0.4143 for the four performance metrics.

Damage Object Detection of Steel Wire Rope-Core Conveyor Belts Based on the Improved YOLOv5

In response to the challenges in detecting damage features in X-ray images of steel wire rope-cores in conveyor belts, such as complex damage shapes, small sizes, low detection precision, and poor generalization ability, an improved YOLOv5 algorithm was proposed. The aim of the model is to accurately and efficiently identify and locate damage in the X-ray images of steel wire rope-cores in conveyor belts. First, the Adaptive Histogram Equalization (AHE) method is used to preprocess the images, reducing the interference of harsh mining environments and improving the quality of the dataset. Second, to better retain image details and enhance the detection ability of damage features, transpose convolutional upsampling is adopted, and the C3 module in the backbone network is replaced by C2f to ensure lightweight network models, meanwhile, it obtains richer gradient flow information and optimizing the loss function. Finally, the improved algorithm is compared with four classical detection algorithms using the damage feature dataset of steel wire rope-core conveyor belts. The experimental result shows that the proposed algorithm achieves an average detection precision of 91.8% and a detection speed of 40 frames per second (FPS) for images collected in harsh mining environments. The designed detection model provides a reference for the automatic recognition and detection of damage to steel wire rope-core conveyor belts.

A Novel Framework of Hybrid Optimization Techniques for Contrast-Enhancement in Cardiac MRI Medical Images

Medical image processing plays a crucial role in diagnosing and treating various diseases, with Magnetic Resonance Imaging (MRI) being a widely used modality. A previous study introduced an algorithm aimed at eliminating noise from MRI images through a filtering process, effectively suppressing mixed noise while preserving important structures like edges. However, limitations in optimizing parameters for image enhancement and occasional edge preservation failures were observed. Addressing the challenge of inadequate contrast enhancement in medical image processing, this study proposes a solution utilizing the hybrid cetacean optimization algorithm and Sand Cat Swarm Optimization (COA-SCSO) to enhance contrast in MRI images. The proposed approach focuses on improving the contrast nature in MRI images to mitigate image degradation during acquisition. Cardiac MRI images from SCMR consensus and AMRG Atlas databases are analyzed, emphasizing the consideration of spatial information for enhancement. The enhancement process involves integrating a cost function with a contrast measure based on multiple metrics to create a novel transformation function. Soft-computing approaches are employed to reduce time complexity, while expanding the search pattern through a transformation function in the spatial domain. This method enhances pixel intensity, contributing to increased image resolution. Performance evaluation of the proposed algorithm incorporates various parameters such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Normalized Absolute Error (NAE), and Structural Similarity Index (SSIM). Results from analysis of ten cardiac MRI images indicate that the proposed algorithm achieves the highest PSNR (98) and SSIM (0.99) values, as well as the lowest NAE (-0.17) and MSE (0.16) values, compared to the particle swarm optimized texture-based histogram equalization (PSOTHE) method and modified sunflower optimization (MSFO) method. Overall, the COA-SCSO approach demonstrates promising results in enhancing contrast in MRI images, offering potential benefits for medical diagnosis and treatment planning. Further research could explore additional optimization techniques and validation on larger datasets to further validate its effectiveness in clinical practice.

A novel intuitionistic fuzzy generator for low-contrast color image enhancement technique

Fuzzy logic systems play a significant role in various fields including image processing. Addressing the challenge of enhancing low-illumination images, which often contain a high degree of uncertain information, is a complex task. To overcome this issue, this study introduces a novel intuitionistic fuzzy generator for enhancing low-contrast images. Initially, a low-illumination image is considered and the image is then fuzzified using a fuzzification technique. The fuzzified image is converted into a intuitionistic fuzzy image using the proposed novel intuitionistic fuzzy generator and then applied a contrast limited adaptive histogram equalization method to obtain the enhanced image. The obtained results are compared with the existing studies. With the outcome of the performance measures such as entropy, no-reference low-light image enhancement evaluation and contrast improvement index, it is perceived that the proposed technique yields a finer results than the previous studies.

An Automated Wavelet Scattering Network Classification Using Three Stages of Cataract Disease

A cataract is an eye disease that causes visual distortion and the late stage of this disease can lead to blindness. It is considered a silent disease that can occur without the appearance of symptoms. Therefore, the most effective way to detect cataracts is through accurate and timely detection to prevent hurting, expensive operations, and to stop blindness. The purpose of this paper is to propose an automated system based on the wavelet scattering network which categorizes the patients into four classes: early cataract, intermediate cataract, late cataract, and non-cataract conditions using 512 images of the ODIR dataset (212 Cataract and 300 of Normal). The first step in this technique is the preprocessing step for the retinal image was a mean filter, which was utilized to reduce the image's noise. The limited contrast adaptive histogram equalization (CLAHE) method was then employed to improve the image's contract level. Then, Low-variance characteristics can be extracted from image data using a wavelet scattering network for use in deep learning applications. In this network, lowpass scaling filters and predefined wavelets are employed. The average accuracy of the suggested method was 100% for four classes (Normal, Early, Moderate, and Severe). The results are promising compared with other similar works.

A New Method Used to Enhance the SPAIR Image of the Spine MRI.

Magnetic resonance imaging (MRI) is a handy diagnostic tool for orthopedic disorders, particularly spinal and joint diseases. The lumbar intervertebral disc is visible in the T1 and T2 weight sequences of the spine MRI, which aids in diagnosing lumbar disc herniation, lumbar spine tuberculosis, lumbar spine tumors, and other conditions. The lumbar intervertebral disc cannot be seen accurately in the Spectral Attenuated Inversion Recovery (SPAIR) due to weaknesses in the fat and frequency offset parameters, which is not conducive to developing the intelligence diagnosis model of medical image. In order to solve this problem, we propose a composite framework, which is first to use the contrast limited adaptive histogram equalization (CLAHE) method to enhance the SPAIR image contrast of the spine MRI and then use the non-local means method to remove the noise of the image to ensure that the image contrast is uniform without losing details. We employ the Information Entropy (IE), Peak signal-to-noise ratio (PSNR), and feature similarity index measure (FSIM) to quantify image quality after enhancement by the composite framework. The outcomes of the experiments' output images and quantitative data indicate that our composite framework is better than others.

Microarray Images Contrast Enhancement and Gridding Using Genetic Algorithm

Background: Microarray is a sophisticated tool that concurrently analyzes the expression levels of thousands of genes, giving scientists an overview of DNA and RNA study. This procedure is divided into three stages: contact with biological samples, data extraction, and data analysis. Because expression levels are disclosed by the interplay of light with fluorescent markers, the data extraction stage relies on image processing methods. To extract quantitative information from the microarray image (MAI), four steps of preprocessing, gridding, segmentation, and intensity quantification are required. During the generation of MAIs, a large number of error-prone processes occur, leading to structural problems and reduced quality in the resulting data, affecting the identification of expressed genes. Methods: In this article, the first stage has been examined. In the preprocessing stage, the contrast of the images is first enhanced using the genetic algorithm, then the source noises that appear as small artifacts are removed using morphology, and finally, to confirm the effect of the contrast enhancement (CE) on the main stages of microarray data processing, gridding is checked on complementary deoxyribonucleic acid MAIs. Results: The comparison of the obtained results with an adaptive histogram equalization (AHE) and multi-decomposition histogram equalization (M-DHE) methods shows the superiority and efficiency of the proposed method. For example, the image contrast of the Genomic Medicine Research Center Laboratory dataset is 3.24, which is 42.91 with the proposed method and 13.48 and 32.40 with the AHE and M-DHE methods, respectively. Conclusions: The performance of the proposed methods for CE is evaluated on 3 databases and a general conclusion is obtained as to which CE method is more suitable for each dataset.

Prediction of Plant Leaf Diseases using Drone and Image Processing Techniques

Plant diseases are one of the major threats to global food production. Efficient monitoring and detection of plant pathogens are instrumental in restricting and effectively managing the spread of the disease and reducing the cost of pesticides. Traditional, molecular, and serological methods that are widely used for plant disease detection are often ineffective if not applied during the initial stages of pathogenesis, when no or very weak symptoms appear. Moreover, they are almost useless in acquiring spatialized diagnostic results on plant diseases. On the other hand, remote sensing (RS) techniques utilizing drones are very effective for the rapid identification of plant diseases in their early stages. Currently, drones, play a pivotal role in the monitoring of plant pathogen spread, detection, and diagnosis to ensure crops’ health status. The advantages of drone technology include high spatial resolution (as several sensors are carried aboard), high efficiency, usage flexibility, and more significantly, quick detection of plant diseases across a large area with low cost, reliability, and provision of high-resolution data. Drone technology employs an automated procedure that begins with gathering images of diseased plants using various sensors and cameras. After extracting features, image processing approaches use the appropriate traditional machine learning or deep learning algorithms. Features are extracted from images of leaves using edge detection and histogram equalization methods. Drones have many potential uses in agriculture, including reducing manual labor and increasing productivity. Drones may be able to provide early warning of plant diseases, allowing farmers to prevent costly crop failures

Classification of Pancreatic Cystic Lesions Using ResNet Deep Learning Network in Confocal Laser Endomicroscopy Videos

Accurate classification of pancreatic cystic lesions is crucial to differentiate mucinous lesions of malignant potential. We utilized the ResNet-50 and ResNet-101 network to develop a model for classification of the pancreatic cystic lesions. A total of 50 videos, 13,425 images, from five types of pancreatic cystic lesions and utilize the image rotation and contrast reversal scheme for the training. We adopt a contrast limited adaptive histogram equalization method onto the test video. Our method can automatically classify the feature type and record the prediction results frame by frame. The method has been evaluated on 18 test videos and achieves an accuracy 94% overall.

Modelling and Analysis of Pufferfish (Torquigener albomaculosus) Circular Nest on Seafloor

This study analyzed the structural features of the circular structures built by the male pufferfish (Torquigener albomaculosus) on the seafloor. This impressive circular structure which is built by male pufferfish exists in different depths and sizes on the seafloor. One of the objectives for the male pufferfish constructing these circular structures is to influence the opposite sex. The morphological features of these circular structures built on the sea floor were analyzed mathematically. This analytical study was performed using photographs received from the sea floor as well as the ratios of circular formations discovered in this context. During the image analysis, the histogram equalization method was used to improve the visuals and reveal the patterns of circular structures. The Hough transform method, which is commonly used in the determination of circular structures, was used in the process of precisely determining the dimensions of the obtained circular structures. The circle size ratios obtained from the images and the circular structure dimension ratios obtained from the observation were examined. When the ratio analysis results from 2 images and 6 observation data were examined, it was observed that significant traces of the golden ratio were seen in these nested circular structures. According to this study’s analysis of the pufferfish nest, which demonstrated proper development, the percentage difference between the golden ratio and the radius of the circular structures was determined to be as low as 0.185%. Additionally, these circular constructions were recreated in 3D while preserving their proportions, and their hydrostatic pressure characteristics were analyzed depending on their actual depths on the seafloor.

Detection of Papilledema Severity from Color Fundus Images using Transfer Learning Approaches

Papilledema is edema in the area where the optic nerve meets the eye as a result of increased pressure inside the head. This disease can result in very serious problems, such as abnormal optical changes, decreased visual acuity, and even permanent blindness if left untreated. In this study, an image processing based solution was presented for the detection of papilledema severity from color fundus images using transfer learning approaches. The image dataset includes 295 papilledema images, 295 pseudopapilledema images, and 779 control images. Histogram equalization and the 3D box filter were used for image preprocessing. The images were enhanced with the histogram equalization method and denoised with the 3D box filter method. Then, the performances of EfficentNet-B0, GoogLeNet, MobileNetV2, NASNetMobile, and ResNet-101 transfer learning approaches were compared. The hold-out method was used to calculate the performance of transfer learning. In the experiments, the MobileNetV2 approach had the highest performance with 0.96 overall accuracy and 0.94 Cohen's Kappa. The results of the experiments proved that the combination of the histogram equalization, the 3D box filter, and the MobileNetV2 transfer learning approach can be used for automatic detection of papilledema severity. Compared to other similar studies that are known in the literature, the overall accuracy was higher.

Effects of Histogram Equalization Method on Some Deep Learning Models in Brain Tumor Classification

Beyin tümörü beyinde bulunan hücrelerin yenilenmesi sırasında oluşan hatalardan dolayı kafatası içerisinde anormal kitle büyümesinin genel adıdır. Beyin tümöründen hayatını kaybeden kişi sayısı gün geçtikçe artmaktadır. Kayıpları azaltmak için hastalığın tedavi planlamasında ve sonuç değerlendirilmesinde erken teşhis hayati bir önem taşımaktadır. Beyin tümör teşhisi için yaygın olarak kullanılan ve beyin içerisindeki dokuları gösteren MR (manyetik rezonans) görüntüleme yöntemi kullanılmaktadır. Geleneksel yöntemlerle MR görüntülerini kullanılarak beyin tümörünü sınıflandırmak beyin yapısı ve içindeki dokuların karmaşık olmasından dolayı zordur. Beyin tümörü sınıflandırmasında son yıllarda popüler olan ve sınıflandırma konusunda yüksek doğruluk oranları yakalayan derin öğrenme mimarileri kullanılmaktadır. Bu çalışmada VGG16,VGG19 ve MobileNet derin öğrenme mimarileri karşılaştırılarak en yüksek doğruluk oranına sahip olan mimarinin belirlenmesi amaçlanmıştır. Bu mimarilerin başarısını artırmak için veri tabanındaki görüntülere histogram eşitleme işlemi uygulanmıştır. Kullanılan veri seti 3590 MR görüntüsünden oluşup dört beyin tümörü sınıfından (glioma, meningioma, tümörsüz, pituitary) oluşmaktadır. Yapılan test ve eğitim sonucunda en yüksek doğruluk MobileNet tarafından elde edilmiştir. Deneysel çalışmalar histogram eşitleme işleminin görüntülerin kalitesini iyileştirerek derin öğrenme mimarilerinin performanslarına olumlu katkılar sunduğunu göstermiştir.

Quantitatively sensitive and resolution-enhancing strategies in optical signal conversion of SPR images using extensive angle-scanning and image-reconstruction

Surface plasmon resonance imaging (SPRi) is a technology developed from SPR that has emerged in response to the growing need for high-throughput and for discovering diverse applications across multiple domains. Despite its potential, SPRi has encountered limitations in sensitivity when handling low-molecular-mass materials, which emphasizes the need for the quantitative processing of SPRi signals. In this study, an SPRi signal conversion process was developed to enhance the contrast of SPR images and convert them into quantitatively analyzed signals. At first, an optimum incident angle of light was determined as an angle that maximized the difference between sums of the brightness % of functionalized regions and the brightness % of background region in the SPR image, which enabled to obtain a distinct SPR image. In addition, the enhancement of contrast and reduction of noise in the SPR image was achieved using Contrast Limited Adaptive Histogram Equalization (CLAHE) method. Then various pixel domains of the SPR images were simultaneously quantified and visualized the multi-pixel processing technique. For validating the process, an SiO2/Au thin film was modified with four different functionalities and applied to the detection of gas molecules. The SPR images processed using the proposed process exhibited sensitive and quantitative information about the interactions of the functionalities with the target gas in the distinct multiple regions, thus, were applicable to the quantitative monitoring of various phenomena occurring on the plasmonic interface via the advantages of SPRi.

Hybrid ResGRU: Effective brain tumour classification approach using of abnormal images

Brain tumor is observed to be grown in irregular shape and presented deep inside the tissues that led to cancer. Human brain tumor identification and categorization are performed with high latency, but also an essential task for the medical experts. The assistance through the automated diagnosis is generally utilized for the advancement in the diagnosis ability in order to get superior accuracy in brain tumor detection. Although the researches are enhancing the brain tumor detection performance, the highly challenging is to segment the brain tumor since it has variability concerning the tumor type, contrast, image modality and also in other factors. To meet up all the challenges, a novel classification method is introduced using segmentation and machine learning approaches. Initially, the required images are collected from benchmark data sources. The input images are undergone for pre-processing stage, where it is done via “Contrast Limited Adaptive Histogram Equalization (CLAHE) and filtering methods”. Further, the pre-processed imagesare given as input to two classifier models as “Residual Network (ResNet) and Gated Recurrent Unit (GRU)”, in which the model provide the result as normal and abnormal images. In the second part, obtained abnormal image acts an input for segmentation step. In segmentation, it is needed to extract the relevant features by texture and spatial features. The resultant features are subjected for optimizing, where the optimal features are acquired through Adaptive Coyote Optimization Algorithm (ACOA). Then, the extracted features are fed into machine learning model like “Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF)” to render the segmented image. Finally, the hybrid classification named Hybrid ResGRUis developed by integrating the ResNet and GRU, where the hyper parameters are tuned optimally using developed ACOA, thus it is used for classifying the abnormal image that belongs to benign stage or malignant stage. The experimental results are evaluated, and its performance is analyzed by various metrics. Hence, the proposed classification model ensures effective segmentation and classification performance.

Intelligent optimization-based pulmonary emphysema detection with adaptive multi-scale dilation assisted residual network with Bi-LSTM layer

Emphysema is a type of chronic disorder that occurs due to the abnormal expansion of the alveoli in the lungs. This causes several difficulties to breathe, and the final stage of emphysema leads to lung cancer. By using pulmonary function tests and Computed Tomography (CT) scans, the progressive destruction of emphysema disease can be addressed. The early-stage detection of emphysema may help to reduce the risk of the patients. The primary diagnosis should be carried out with the help of CT and spirometry analysis to reduce mortality rates. In the computer vision-based approaches, the inter-observer and intra-observer variations are difficult to analyze. To overcome these complications, an enhanced emphysema disease detection model with adaptive deep-structured architecture is implemented. At first, the input images are undergone, where histogram equalization, pre-processing stage, filtering technique, and Contrast Limited Adaptive Histogram Equalization methods are utilized. Then, the pre-processed images are fed into the lung segmentation process by using Fuzzy C-Means Clustering (FCM) and Adaptive Region Growing. Those segmented images are provided as input for the proposed emphysema detection by utilizing Adaptive Multi-Scale Dilation Assisted Residual Network with Bi-LSTM Layer (AMSD-RN-Bi-LSTM) layer, in which the constraints are optimized by using Improved Honey Badger Algorithm (IHBA). Through the experimental analysis, the proposed pulmonary emphysema detection model shows the tendency to give a rapid diagnosis of a disease that aids to identify the disease and diagnose them effectively.

Image Processing Algorithm Design of Agricultural Internet of Things Platform Based on Big Data Analysis

Abstract To solve the problems of low signal to noise ratio (SNR), poor processing effect, and long processing times for traditional image processing algorithms, an image processing algorithm for the agricultural Internet of Things platform based on big data analysis was designed. The big data analysis method was used for agricultural low-quality image recognition, and the Internet of Things platform uses sparse representation and a complete dictionary of low-quality image denoising processing with an improved histogram equalization method to enhance the image denoising result and get a high-quality image. Through this, the image processing algorithm design of the agricultural Internet of Things platform was completed. Experimental results show that the algorithm has high SNR and a good image processing effect, and the image processing time is always below 0.7 s, which meets research expectations.

Comparison of Image Enhancement Methods for Diabetic Retinopathy Screening

The most common factor contributing to visual abnormalities that result in blindness is known as diabetic retinopathy (DR). Retinal fundus scanning, a non-invasive method that is integral to the picture pre-processing phase, can be used to identify and monitor DR. Low intensity, irregular lighting, and inhomogeneous color are some of the main issues with DR fundus photographs. Analysis of aberrant characteristics on retinal fundus images to identify diabetic retinopathy is one of the key responsibilities of image enhancement. However, a variety of approaches have been created and it is unknown whether one is best suited for use with images of the retinal fundus. This study investigated various image enhancement methods in order to see aberrant abnormalities on retinal fundus pictures more clearly. This study investigated various image enhancement methods in order to see aberrant abnormalities on retinal fundus pictures more clearly. The contrast-limited adaptive histogram equalization (CLAHE) method, the gray-level slicing method, the median filtering method, and the low light method are image improvement methods used to enhance images of the retinal fundus. The parameters Natural Image Quality Evaluator (NIQE), Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and entropy will be used to assess each image enhancement technique's performance. An ophthalmologist from Sains University Hospital (HUSM) provided the image data. The findings indicate that while each technique has its own benefits, the CLAHE technique, with a standard deviation MSE of 0.0004, is the best.

Brightness segmentation-based plateau histogram equalization algorithm for displaying high dynamic range infrared images

Third-generation thermal cameras produce images with high dynamic range (HDR), low contrast, and blurry edges, which makes them difficult to visualize on traditional display devices. Thus, tone mapping methods are required to adapt the recorded signal to the display in order to maintain, and possibly improve, object’s visibility and contrast. Since the traditional global tone mapping methods cannot take account of the trade-off between clearly displaying the details of dark regions and bright regions of the infrared image, this paper proposes a global tone mapping algorithm based on brightness segmentation for the enhancement and display of HDR infrared images. Firstly, the HDR infrared image is divided into different brightness regions in accordance with the characteristics of the human visual system’s ability to perceive brightness. Secondly, the determination of the boundary value of each brightness region is optimized by combining the gray statistics of the HDR infrared image, to achieve the adaptive brightness region segmentation of the infrared image from various scenes. Thirdly, the plateau histogram equalization method is designed to enhance different brightness regions separately, according to their brightness levels. Finally, the enhanced brightness regions are recombined to produce a more visually pleasing output image. The effectiveness of the proposed method is analyzed of HDR infrared images taken from different scenes. A comparison of the proposed method with well-established global tone mapping techniques is provided, along with subjective and objective evaluation methods that demonstrate the potential benefits of this method in enhancing brightness, compressing dynamic range, and improving global contrast.