Adaptive Histogram Equalization Method Research Articles

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

إعتام عدسة العين وهو مرض يصيب العين حيث يسبب تشوهًا بصريًا ويمكن أن تؤدي المرحلة المتأخرة من هذا المرض إلى العمى. ويعتبر مرضًا صامتًا يمكن أن يحدث دون ظهور الأعراض. لذلك ، فإن الطريقة الأكثر فاعلية للكشف عن إعتام عدسة العين هي من خلال الكشف الدقيق في الوقت المناسب لمنع الأذى والعمى والعمليات المكلفة. ان الغرض من هذه البحث هو اقتراح نظام آلي يعتمد على شبكة نثر المويجات التي تصنف المرضى إلى أربع فئات: إعتام عدسة العين المبكر وإعتام عدسة العين المتوسط وإعتام عدسة العين المتأخر وعدم إعتام عدسة العين باستخدام 512 صورة لقاعدة البيانات ODIR (212 شخص مصاب و300 شخص غير مصاب). الخطوة الأولى في هذه التقنية هي خطوة المعالجة المسبقة لصورة الشبكية التي كانت عبارة عن مرشح المتوسط ، والذي تم استخدامه لتقليل ضوضاء الصورة. ثم بعد ذلك استخدام طريقة معادلة الرسم البياني التكيفية محدودة التباين (CLAHE) لتحسين مستوى عينات الصورة. بعد ذلك ، يمكن استخراج الخصائص منخفضة التباين من بيانات الصورة باستخدام شبكة تشتت المويجات لاستخدامها في تطبيقات التعلم العميق. في هذه الشبكة ، يتم استخدام مرشحات تحجيم تمرير منخفض وموجات موجية محددة مسبقًا. كان متوسط دقة الطريقة المقترحة 100٪ لأربع فئات (غير مصاب، مبكر، متوسط، شديد). علما ان النتائج موعودة مقارنة مع أعمال أخرى مماثلة.

SegNet‐VOLO model for classifying microplastic contaminants in water bodies

AbstractIn recent times, microplastics (MPs) have emerged as notable contaminants within several environments, especially in water bodies. The characterization and description of MPs necessitate extensive and laborious analytical methods, making this part of MPs research an essential issue. In this research, SegNet‐Vision Outlooker (VOLO), a computer vision and deep learning (DL)‐based model, is proposed for detecting and classifying MPs present in a water environment. This research model includes step‐by‐step processes such as data collection, preprocessing, filtering and enhancement, augmentation, segmentation, feature extraction, and classification for detecting MPs. The key objective of this research model is to improve the classification accuracy in detecting MPs and to validate the model's effectiveness in handling holographic images. The Holographic Image MPs dataset is collected and used to evaluate the model. In preprocessing, image rescaling is performed to match the proposed model's input resolution as 224 × 224. After rescaling, the images are applied to remove noise using a bilateral filtering technique. The contrast‐limited adaptive histogram equalization (CLAHE) method is applied to enhance the image with better contrast and brightness, which helps the model to segment and classify the images accurately. The enhanced images are applied to the SegNet model for segmentation, which segmented the images according to the MP classes. Based on the segmented images, the VOLO‐D1 model extracted the features and classified the images to detect the MPs present in the images. The SegNet‐VOLO model attained 97.70% detection rate, 98.26% accuracy, 98.13% F1‐score, and 98.62% precision. These performances are compared with the various existing models discussed in the review, where the research model outperformed all the models with better performances.

Retinex Jointed Multiscale CLAHE Model for HDR Image Tone Compression

Tone-mapping algorithms aim to compress a wide dynamic range image into a narrower dynamic range image suitable for display on imaging devices. A representative tone-mapping algorithm, Retinex theory, reflects color constancy based on the human visual system and performs dynamic range compression. However, it may induce halo artifacts in some areas or degrade chroma and detail. Thus, this paper proposes a Retinex jointed multiscale contrast limited adaptive histogram equalization method. The proposed algorithm reduces localized halo artifacts and detail loss while maintaining the tone-compression effect via high-scale Retinex processing. A performance comparison of the experimental results between the proposed and existing methods confirms that the proposed method effectively reduces the existing problems and displays better image quality.

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 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.

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.

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. 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. 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. 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.

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.

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.

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.

A Novel Gabor Filtering and Adaptive Histogram Equalization Method for Improving Images

The correct information may only sometimes be effectively conveyed by images due to various factors, such as excessively bright or dark lighting and low or high contrast. As a result, picture improvement has become an essential part of digital image processing. This proposed method aims to develop an algorithm for improving photos captured in dark environments. This letter presents a new picture-enhancing approach that combines median and Gabor filtering using the wavelet domain with histogram equalization working over a spatial domain. The proposed method in this paper combines spatial and transformed domains for image enhancement and has been simulated using MATLAB. The simulation results of two different photos show that the suggested approach extends the histogram over a wide range of grayscale, offering a superior improvement to the original image. The novel proposed algorithm aims to improve image quality and visibility, making identifying essential details within the image easier. Further, the proposed technique's success is manifested by examining the produced photos' contrast and brightness. The findings reveal that the suggested technique beats the other strategies for improving low-contrast photos.

An optimized XGBoost technique for accurate brain tumor detection using feature selection and image segmentation

An abnormal multiplication of cells in the brain forms malignant and benign brain tumors. Malignant brain tumors are more prevalent than benign ones. Detecting a tumor’s physical features may be tedious and time-consuming for medical experts due to the complexity of a tumor’s structure and noise growth in Magnetic Resonance Imaging (MRI) data. Detecting and localizing tumors at an early stage in their growth is crucial. Complex structures and precise clinical identification using segmentation are considered the norm in medical imaging. As the quantity of images rises, the radiologist’s manual tumor assessment might result in an incorrect conclusion. Due to the likelihood of human error, the evaluation and categorization of medical images require an automated method. This paper optimizes the eXtreme Gradient Boosting (XGBoost) approach through image processing and feature selection for the reliable identification of brain tumors. Images are improved using the Contrast-Limited Adaptive Histogram Equalization (CLAHE) method. The K-Means algorithm divides images into sections. This segmentation assists in pinpointing the specific area of interest. Particle Swarm Optimization (PSO) is used to choose characteristics. The data is classified by XGBoost, Naive Bayes, and the Iterative Dichotomiser 3 (ID3). According to the experimental results, the suggested PSO-XGBoost model achieved 97% accuracy, 97% specificity, 98% precision, and 98% recall.

UWV-Yolox: A Deep Learning Model for Underwater Video Object Detection.

Underwater video object detection is a challenging task due to the poor quality of underwater videos, including blurriness and low contrast. In recent years, Yolo series models have been widely applied to underwater video object detection. However, these models perform poorly for blurry and low-contrast underwater videos. Additionally, they fail to account for the contextual relationships between the frame-level results. To address these challenges, we propose a video object detection model named UWV-Yolox. First, the Contrast Limited Adaptive Histogram Equalization method is used to augment the underwater videos. Then, a new CSP_CA module is proposed by adding Coordinate Attention to the backbone of the model to augment the representations of objects of interest. Next, a new loss function is proposed, including regression and jitter loss. Finally, a frame-level optimization module is proposed to optimize the detection results by utilizing the relationship between neighboring frames in videos, improving the video detection performance. To evaluate the performance of our model, We construct experiments on the UVODD dataset built in the paper, and select mAP@0.5 as the evaluation metric. The mAP@0.5 of the UWV-Yolox model reaches 89.0%, which is 3.2% better than the original Yolox model. Furthermore, compared with other object detection models, the UWV-Yolox model has more stable predictions for objects, and our improvements can be flexibly applied to other models.

The Analysis of Underwater Imagery System for Armor Unit Monitoring Application

The placement of armor units for breakwaters in Indonesia is still done manually, which depends on divers in each placement of the armor unit. The use of divers is less effective due to limited communication between divers and excavator operators, making divers in the water take a long time. This makes the diver's job risky and expensive. This research presents a vision system to reduce the diver's role in adjusting the position of each armor unit. This vision system is built with two cameras connected to a mini-computer. This system has an image improvement process by comparing three methods. The results obtained are an average frame per second is 20.71 without applying the method, 0.45 fps for using the multi-scale retinex with color restoration method, 16.75 fps for applying the Contrast Limited Adaptive Histogram Equalization method, 16.17 fps for applying the Histogram Equalization method. The image quality evaluation uses the underwater color quality evaluation with 48 data points. The method that has experienced the most improvement in image quality is multi-scale retinex with color restoration. Forty data have improved image quality with an average of 14,131, or 83.33%. The number of images that experienced the highest image quality improvement was using the multi-scale retinex with color restoration method. Meanwhile, for image quality analysis based on Underwater Image Quality Measures, out of a total of 48 images, the method with the highest value for image quality is the contrast limited adaptive histogram equalization method. 100% of images have the highest image matrix value with an average value is 33.014.

Literature Review on X-Ray Image Enhancement

In common, a raw X-ray image holds bad quality of the image if obtained directly coming out of a digital flat detector. It isn't satisfactory for treatment planning as well as diagnosis. For this image, enhancement is required. There are distinct ways through which image enhancement takes place. It is one of the preprocessing techniques helpful for moving further into treatment planning. Common methodologies like N-CLAHE which works with local enhancement and global enhancement can make an image look more extensive and more realistic. This technique consists of two main steps. Firstly, intensity correction of the raw image is encountered by the log-normalization function which adjusts the intensity contrast of the image dynamically. Secondly, the Contrast Limited Adaptive Histogram Equalization (CLAHE) method is used for enhancing small details,texture and local contrast of the images. For purpose of the survey, light is thrown on some of the wellknown developed models, the strategies used, and the shortcomings which were encountered in the development. This review will help researchers to further make advancements in this field

Identifying License Plates in Distorted Vehicle Images: Detecting Distorted Vehicle Licence Plates Using a Novel Preprocessing Methods With Hybrid Feature Descriptors

Intelligent transportation systems (ITSs) play a key role in many people’s lives with different aspects. Most of the ITS applications include a system for detecting license plates (LPs) in transportation vehicles. A new framework for such detection systems is introduced in this article. It includes a novel technique for preprocessing, extraction, and detection stages to identify LPs from distorted vehicle images. An efficient preprocessing method is developed in this study. An enhanced, contrast-limited adaptive histogram equalization method for filtering the unwanted LP features is proposed. At the features-extraction stage, strong hybrid features from extended local binary patterns with a median robust pattern descriptor and speeded-up robust feature descriptor are applied to extract complicated features from LP areas. Those hybrid features can enhance the useful information, and the detection system performed well under difficult scenarios. In the detection stage, the trained model by an extreme learning machine (ELM) classifier, with mean-shift algorithm, is used as a detector to decide output results. Performances of the proposed framework were compared with other classifiers using true- and false-positive rates. The system’s performance improvements by the proposed method were also compared with our previous methods and the existing detection methods in the literature. The experiments on an English car LP’s database and other language vehicle images showed that the proposed method made significant improvements to the accuracy and runtime speed for the detection system under difficult image conditions.

Traffic sign recognition with low-carbon technology in nighttime environment based on deep learning

Abstract Aiming at the decrease in the accuracy of traffic sign recognition due to dim light in the night environment, this paper proposes an improved you only look once version 5 (YOLOv5) algorithm to reduce carbon emissions. An improved adaptive histogram equalization method is designed to adjust the brightness and contrast of the image and highlight the detail information of traffic signs. In response to the higher requirements of the driving assistance system on the recognition model processing speed, the model is lightened and the standard convolution method of the backbone network is designed as a depth-separable convolution method, which greatly reduces the number of model parameters. To address the problem of feature loss during model learning, an improved feature pyramid AAM SPPF path aggregation network (AS-PAN) structure is proposed to enhance the learning capability of the model by adding an adaptive attention module to the Neck head and a spatial pyramid pooling module before its P3 and P4 outputs. Finally, the traditional non-maximum suppression (NMS) generates prediction frames by replacing the traditional NMS with weighted frame fusion weighted boxes fusion (WBF), which changes all possibility target frames from discard to fusion. Experiments demonstrate that the improved algorithm achieves improved detection accuracy, decreased processing time for a single image and low carbon emissions in the traffic sign recognition process compared with the original YOLOv5 algorithm in a self-built nighttime environment dataset.