Histogram Equalization Research Articles

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.

Segnet with Unet3+ and EfficientNet: a novel framework of brain tumour segmentation and classification model by multiscale attention-based deep learning techniques with hybrid heuristic improvement using 3D MRI brain images

ABSTRACT An adaptive deep learning is recommended to segment and classify the brain tumor using 3D MRI images. Initially, the original 3D MRI images are gathered and fed into pre-processing, which is accomplished using the 'Contrast Limited Adaptive Histogram Equalization (CLAHE)' technique. The pre-processed image is given as input to 'Multiscale Self-guided Attention based Hybrid Adaptive Networks (MSA-HAN)', where it encompasses Segnet with Unet3+ for segmentation. In this model, certain parameters are optimized using the Position Updating of Black Widow and Shark Smell (PUBWSS). Finally, the segmented region is subjected into the Adaptive Atrous Spatial Pyramid Pooling (ASPP) based EfficientNet (AA-ENet) that considers MobileNet and DenseNet. Thus, the model's performance is evaluated, and results are carried out with diverse measures. The performance of the offered method shows 95.74%, and 95.80% in terms of accuracy and F1-score. Hence, the results declare that the developed model achieves higher segmentation accuracy than other approaches.

Local Adaptive Image Equalization

This paper presents a comprehensive approach to image enhancement, targeting the enhancement of contrast and reduction of noise in digital images. Leveraging state-of-the-art algorithms, the proposed methodology encompasses a strategic pipeline. Initially, the images undergo Histogram Equalization, a fundamental technique, to globally enhance contrast. Building upon this foundation, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied to achieve localized contrast enhancement, ensuring optimal balance and preservation of image details. Furthermore, the Adaptive Gamma Correction with Weighting Distribution (AGCWD) algorithm is integrated to fine-tune the enhanced images, dynamically adjusting gamma values to suppress noise and amplify visual features. The implementation harnesses Python with OpenCV and Flask frameworks, facilitating seamless integration and accessibility. Through rigorous experimentation and comparative analysis, the efficacy of the proposed approach is demonstrated, showcasing substantial improvements in image quality and fidelity. The findings underscore the practical utility and efficacy of the proposed image enhancement system, positioning it as a valuable tool for various real-world applications in image processing and computer vision domains. Keywords: Image Enhancement ,Noise Reduction ,Histogram Equalization, CLAHE, Adaptive Gamma Correction, OpenCV Library, Flask Web Framework , Image Fidelity.

Deep Seabed Image Enhancement Algorithm for Manned Submersible Acquisition

Objective: Due to the problems of light propagation underwater, such as scattering and absorption, which leads to low contrast, color distortion and blurring of details in the images obtained underwater, this phenomenon is more serious in the deep sea, and most undersea map images collected by manned submersibles during deep-sea exploration exhibit these issues. To address these problems, an underwater image fusion enhancement algorithm based on color correction and image sharpening is proposed for image enhancement and restoration. Methods: The automatic color enhancement algorithm is used to correct and enhance the color of the underwater image. Subsequently, the RGB three channels are corrected by gamma filtering. The RGB space is then converted to the Lab space, and the L channel is processed by the Contrast Limited Adaptive Histogram Equalization algorithm to enhance the luminance. At the same time, the color-corrected image is image-sharpened by using unsharpened mask algorithms. Finally, image fusion is performed by using the algorithm based on the fusion of guided filters. Results: The experimental results on several underwater images under different scenarios show that the enhancement effect achieved by this algorithm is better than the comparison algorithm. In the subjective aspect, the color and details of the images are better balanced and enhanced. In the objective aspect, quantitative evaluations are carried out in three aspects: Information entropy (IE), underwater image quality measure (UIQM), and underwater color image quality evaluation (UCIQE), in which the IE is quantitatively evaluated. Regarding the UCIQE, the proposed algorithm has a better effect than other algorithms in terms of IE, UIQM, and UCIQE. Specifically, compared to the original image, the proposed algorithm shows improvements of 33.4%, 1.45 times, and 54.4% or more in mean value. Conclusion: The results show that the algorithm has good results in image processing for underwater environments, with enhancement and restoration of images acquired through manned submersibles.

Enhancing Visibility Through Image Defogging

This project introduces a method for enhancing visibility in foggy images through histogram equalization. By leveraging the LAB color space, the method separates luminance and chrominance components, applying histogram equalization solely to the luminance channel. This process effectively enhances contrast and brightness while preserving color fidelity. Experimental results demonstrate significant visibility improvements in foggy scenes, with computational efficiency suitable for real-time applications. This approach holds promise for enhancing visibility in critical domains like transportation safety and outdoor surveillance. Keywords: Fog Removal, Atmospheric Haze, Luminance Channel, Image Enhancement, Noise Reduction ,Histogram Equalisation, Color Fidelity.

AN AUTOMATED BREAST CANCER DETECTION BY HEURISTIC-BASED ENSEMBLE DEEP CLASSIFIER USING MAMMOGRAM AND TOMOSYNTHESIS IMAGES

Nowadays, breast cancer is the most founded cancer among women. Moreover, 2.3 million new cases of breast cancer have been detected among women since 2020 as reported by World Health Organization (WHO). Many research studies based on breast cancer mainly aim at ultrasound, mammography, and Magnetic Resonance Images (MRI). However, there exist certain limitations such as lack of access for the detection of disease in rural and remote cities and insufficient knowledge about the availability of computer-aided systems. In order to overcome the problem, it is necessary to develop an effective detection model for breast cancer. For the experimentation, the input images like mammogram images and tomosynthesis images are garnered from the benchmark online resources. The input image further undergoes the pre-processing stage, which is made by using “Contrast Limited Adaptive Histogram Equalization (CLAHE) and histogram equalization (HE)”. Then, the pre-processed images are further given as input to the segmentation, in which deeplabv3 is deployed. Consequently, with the assistance of segmented images, the relevant features like “texture features, color features, shape features, deep features, statistical features and morphological features” are extracted. Then, these obtained features are used in weighted feature selection, where the optimal feature selection is performed, and weights are optimized by the Enhanced Adaptive Prey Location-Based Pelican Optimization Algorithm (APL-POA). Finally, the weighted accurate features are given as input to the Ensemble Deep Learning (EDC) model. The ensemble model is structured by “Deep Neural Network (DNN), Recurrent Neural Network (RNN), Bidirectional Long Short-Term Memory (Bi-LSTM), Deep Temporal Convolution Networks (DTCN) and Gated Recurrent Unit (GRU)”, in which the hyperparameters of every classifier are smoothened optimally by the enhanced APL-POA algorithm. Through the experimental analysis, the proposed work tends to provide an improved classification rate and rapid detection of disease that aids in better diagnosis of the patients.

The development and usability test of an automated fish counting system based on CNN and contrast limited histogram equalization

The aquaculture industry has rapidly grown over the year. One pertinent aspect is the ability of the aquaculture farm management to accurately count the fish populations to provide effective feeding and the control of breeding density. The current practice of counting the fish manually increased the hatchery workers workload and led to inefficiency. The presented work proposed an intelligent, web-based fish counting system to assist hatchery workers in counting fish from images. The methodology consists of two phases. First, an intelligent fish counting engine is developed. The captured image was first enhanced using the contrast limited adaptive histogram equalization. A deep learning architecture in the form of you only look once (YOLO)v5 is used to generate a model to identify and count fish on the image. Second, a web-based application is developed to implement the developed fish counting engine. When applied to the test data, the developed engine recorded a precision of 98.7% and a recall of 65.5%. The system is also evaluated by hatchery workers in the University Malaysia Sabah fish hatchery. The results of the usability and functionality evaluations indicate that the system is acceptable, with some future work suggested based on the feedback received.

Large-scale spatial data visualization method based on augmented reality

BackgroundA task assigned to space exploration satellites involves detecting the physical environment within a certain space. However, space detection data are complex and abstract. These data are not conducive for researchers' visual perceptions of the evolution and interaction of events in the space environment. MethodsA time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time, and the corresponding relationships between data location features and other attribute features were established. A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data. The visualization process is optimized for rendering by merging materials, reducing the number of patches, and performing other operations. ResultsThe results of sampling, feature extraction, and uniform visualization of the detection data of complex types, long duration spans, and uneven spatial distributions were obtained. The real-time visualization of large-scale spatial structures using augmented reality devices, particularly low-performance devices, was also investigated. ConclusionsThe proposed visualization system can reconstruct the three-dimensional structure of a large-scale space, express the structure and changes in the spatial environment using augmented reality, and assist in intuitively discovering spatial environmental events and evolutionary rules.

Enhancing assisted diagnostic accuracy in scalp psoriasis: A Multi-Network Fusion Object Detection Framework for dermoscopic pattern diagnosis.

Dermoscopy is a common method of scalp psoriasis diagnosis, and several artificial intelligence techniques have been used to assist dermoscopy in the diagnosis of nail fungus disease, the most commonly used being the convolutional neural network algorithm; however, convolutional neural networks are only the most basic algorithm, and the use of object detection algorithms to assist dermoscopy in the diagnosis of scalp psoriasis has not been reported. Establishment of a dermoscopic modality diagnostic framework for scalp psoriasis based on object detection technology and image enhancement to improve diagnostic efficiency and accuracy. We analyzed the dermoscopic patterns of scalp psoriasis diagnosed at 72nd Group army hospital of PLA from January 1, 2020 to December 31, 2021, and selected scalp seborrheic dermatitis as a control group. Based on dermoscopic images and major dermoscopic patterns of scalp psoriasis and scalp seborrheic dermatitis, we investigated a multi-network fusion object detection framework based on the object detection technique Faster R-CNN and the image enhancement technique contrast limited adaptive histogram equalization (CLAHE), for assisting in the diagnosis of scalp psoriasis and scalp seborrheic dermatitis, as well as to differentiate the major dermoscopic patterns of the two diseases. The diagnostic performance of the multi-network fusion object detection framework was compared with that between dermatologists. A total of 1876 dermoscopic images were collected, including 1218 for scalp psoriasis versus 658 for scalp seborrheic dermatitis. Based on these images, training and testing are performed using a multi-network fusion object detection framework. The results showed that the test accuracy, specificity, sensitivity, and Youden index for the diagnosis of scalp psoriasis was: 91.0%, 89.5%, 91.0%, and 0.805, and for the main dermoscopic patterns of scalp psoriasis and scalp seborrheic dermatitis, the diagnostic results were: 89.9%, 97.7%, 89.9%, and 0.876. Comparing the diagnostic results with those of five dermatologists, the fusion framework performs better than the dermatologists' diagnoses. Studies have shown some differences in dermoscopic patterns between scalp psoriasis and scalp seborrheic dermatitis. The proposed multi-network fusion object detection framework has higher diagnostic performance for scalp psoriasis than for dermatologists.

Design of Novel Brain Tumor Segmentation System Using Hybrid Heuristic-Aided Multiscale Self-Guided Attention Mechanism-Based Adaptive Unet+++ with 3D Brain MRI Images

Segmentation of brain tumors attains great importance in the medical industry. As the brain tumor causes an earlier death, detection and diagnosis are required. Generally, brain tumor diagnosis is done by considering Magnetic Resonance Imaging (MRI) because it has superficial feature information. In the existing works, the different segmentation process is analyzed, yet it is more time-consuming, and has complexities. Thus, building of an efficient segmentation model is a quite challenging task. As the former implemented models are not in place to segment the abnormal region properly, it suggests developing an effective automated model using recently emerged techniques of deep learning. To surmount such challenging factors, a novel 3D brain tumor segmentation model is proposed with hybrid heuristic development. Initially, the brain images are collected from the standard benchmark datasets. The collected brain images are pre-processed using the adaptive technique of Contrast Limited Adaptive Histogram Equalization (CLAHE). The pre-processed images are segmented with the developed Multiscale Self-Guided Attention Mechanism-based Adaptive UNet3[Formula: see text] (MSGAM-AUNet3[Formula: see text]), where the parameters are optimized with the hybrid optimization strategy of Modified Path Finder Coyote Optimization (MPFCO) to elevate the segmentation performance. The experimental analysis is carried out to estimate the efficiency of the developed framework with the comparison using diverse segmentation techniques.

Analysis of Pneumonia on Chest X-Ray Images Using Convolutional Neural Network Model iResNet-RS

Pneumonia, a prevalent inflammatory condition affecting lung tissue, poses a significant health threat across all age groups and remains a leading cause of infectious mortality among children worldwide. Early diagnosis is critical in preventing severe complications and potential fatality. Chest X-rays are a valuable diagnostic tool for pneumonia; however, their interpretation can be challenging due to unclear images, overlapping diagnoses, and various abnormalities. Consequently, expedient, and accurate analysis of medical images using computer-aided methods has become crucial. This research proposes a Convolutional Neural Network (CNN) model, specifically the ResNet-RS Model, to automate pneumonia identification. The Contrast Limited Adaptive Histogram Equalization (CLAHE) technique enhances image contrast and highlights abnormalities in pneumonia images. Additionally, data augmentation techniques are applied to expand the image dataset while preserving the intrinsic characteristics of the original images. The proposed methodology is evaluated through three testing scenarios, employing chest X-ray images and pneumonia dataset. The third testing scenario, which incorporates the ResNet-RS model, CLAHE preprocessing, and data augmentation, achieves superior performance among these scenarios. The results show an accuracy of 92% and a training loss of 0.0526. Moreover, this approach effectively mitigates overfitting, a common challenge in deep learning models. By leveraging the power of the ResNet-RS model, along with CLAHE preprocessing and data augmentation techniques, this research demonstrates a promising methodology for accurately detecting pneumonia in chest X-ray images. Such advancements contribute to the early diagnosis and timely treatment of pneumonia, ultimately improving patient outcomes and reducing mortality rates.

Classification of COVID-19 on Chest X-Ray Images Using Deep Learning Model with Histogram Equalization and Lung Segmentation

Classification of COVID-19 on Chest X-Ray Images Using Deep Learning Model with Histogram Equalization and Lung Segmentation

A proposed CLCOA Technique Based on CLAHE using Cat Optimized Algorithm for Plants Images Enhancement

Image Enhancement is one of the mainly significant with complex techniques in image study. The purpose of image enhancement is to advance the optical presence of an image, or to support a “improved convert representation for future mechanized image processing. Various images similar medical images, satellite images, natural with even real life photographs which have a lowly contrast and noise. This study presents a new enhancement technique based on standard contrast limited adaptive histogram equalization (CLAHE) technique for image enhancement which its name CLCOA. The suggested technique depends on augmentation of swarm intelligence via using Cat Swarm Optimization algorithm (CSO). The swarm intelligence is used to obtain the optimal structure of CLAHE technique. Tomato plant images have used and applied as dataset because of its important and influence in our life. For fair analysis of two techniques, Absolute Mean Brightness Error (AMBE), peak signal-to-noise ratio (PSNR), entropy and Contrast Gain of fundus images are analyzed by using MATLAB. The results show that performance of the proposed technique reveals the efficiently and robustness when compared results of standard technique.

NEW APPROACH FOR BREAST CANCER DETECTION- BASED MACHINE LEARNING TECHNIQUE

The leading cause of cancer-related mortality is breast cancer. Breast cancer detection at an early stage is crucial. Data on breast cancer can be diagnosed using a number of different Machine learning approaches. Automated breast cancer diagnosis using a Machine Learning model is introduced in this research. Features were selected using Convolutional Neural Networks (CNNs) as a classifier model, and noise was removed using Contrast Limited Adaptive Histogram Equalization (CLAHE). On top of that, the research compares five algorithms: Random Forest, SVM, KNN, Naïve Bayes classifier, and Logistic Regression. An extensive dataset of 3002 combined images was used to test the system. The dataset included information from 1400 individuals who underwent digital mammography between 2007 and 2015. Accuracy and precision are the metrics by which the system's performance is evaluated. Due to its low computing power requirements and excellent accuracy, our suggested model is shown to be quite efficient in the simulation results.

Accurate Recognition of Jujube Tree Trunks Based on Contrast Limited Adaptive Histogram Equalization Image Enhancement and Improved YOLOv8

The accurate recognition of tree trunks is a prerequisite for precision orchard yield estimation. Facing the practical problems of complex orchard environment and large data flow, the existing object detection schemes suffer from key issues such as poor data quality, low timeliness and accuracy, and weak generalization ability. In this paper, an improved YOLOv8 is designed on the basis of data flow screening and enhancement for lightweight jujube tree trunk accurate detection. Firstly, the key frame extraction algorithm was proposed and utilized to efficiently screen the effective data. Secondly, the CLAHE image data enhancement method was proposed and used to enhance the data quality. Finally, the backbone of the YOLOv8 model was replaced with a GhostNetv2 structure for lightweight transformation, also introducing the improved CA_H attention mechanism. Extensive comparison and ablation results show that the average precision of the quality-enhanced dataset over that of the original dataset increases from 81.2% to 90.1%, and the YOLOv8s-GhostNetv2-CA_H model proposed in this paper reduces the model size by 19.5% compared to that of the YOLOv8s base model, with precision increasing by 2.4% to 92.3%, recall increasing by 1.4%, mAP@0.5 increasing by 1.8%, and FPS being 17.1% faster.

A New Method for Extracting Refined Sketches of Ancient Murals.

Mural paintings, as the main components of painted cultural relics, have essential research value and historical significance. Due to their age, murals are easily damaged. Obtaining intact sketches is the first step in the conservation and restoration of murals. However, sketch extraction often suffers from problems such as loss of details, too thick lines, or noise interference. To overcome these problems, a mural sketch extraction method based on image enhancement and edge detection is proposed. The experiments utilize Contrast Limited Adaptive Histogram Equalization (CLAHE) and bilateral filtering to enhance the mural images. This can enhance the edge features while suppressing the noise generated by over-enhancement. Finally, we extract the refined sketch of the mural using the Laplacian Edge with fine noise remover (FNR). The experimental results show that this method is superior to other methods in terms of visual effect and related indexes, and it can extract the complex line regions of the mural.

Adaptive histogram equalization with visual perception consistency

Histogram equalization (HE) is a well-established method for image contrast enhancement due to its simplicity and effectiveness. However, it suffers from three main shortcomings, i.e., over-enhancement, under-enhancement and mean shift. To address these issues, this paper proposes a systematic scheme, that is, adaptive histogram equalization with visual perception consistency (AHEVPC). Firstly, a novel histogram correction model is designed to get the optimal controlling parameters, which specifically address the aforementioned issues of HE. Besides, considering the subjective perception of the initial output of the model, two strategies are proposed to make the enhanced image more natural and comfortable. Finally, histogram equalization is applied to the modified histogram. Extensive experimental results demonstrate that the proposed scheme is reasonable and effective, and outperforms several state-of-the-art methods in terms of subjective and objective metrics.

Comparative Analysis of Early Diabetic Retinopathy Detection: Enhanced Minimal CNN Vs VGG Architecture on CLAHE- Preprocessed Retinal Images

Ocular impairment is one of the prominent problems affecting middle-aged individuals due to uncontrolled blood sugar levels, commonly known as Diabetic Retinopathy (DR). The small abnormalities in the retinal capillaries, called microaneurysms and intra retinal bleeding, are the initial symptoms of Diabetic Retinopathy. Clinically recognizing diabetic retinal disease is a time-consuming and difficult process due to limitations in resources and experienced doctors. Early detection is crucial in avoiding the progression of Diabetic Retinopathy, highlighting the importance of an automated DR detection method to identify symptoms in its early stages. In this paper, researchers developed an unfamiliar framework known as Enhanced Minimal Convolutional Neural Network (EMCNN) to classify Mild-DR and No-DR ophthalmic photos using a binary classification process. The proposed new model EMCNN is compared with the migration learning method using the existing framework VGG16 and VGG19 in terms of precision and effectiveness. Before being sent across the network, the fundus pictures underwent preprocessing using the Contrast Limited Adaptive Histogram Equalization (CLAHE) tactic. EMCNN is an experimental model that enjoys a minimum number of layers and batch normalization to minimize the training effort. The EMCNN model achieved 94.89% accuracy using 3100 image dataset which is a remarkable improvement when compared with VGG architectures since the VGG architecture is trained with millions of images

Analysis of Image Processing Algorithms for Water Surface Garbage Cleaning Robots

To facilitate the observation of the water surface conditions in front of the water surface garbage cleaning robots during navigation, an image transmission function was designed using cameras and wireless transmission equipment. However, due to the influence of water surface lighting, the transmitted images were unclear. In this paper, five image processing algorithms were written in Python language using the OpenCV library: Histogram Equalization, Gray World Algorithm, Retinex-based Enhancement Algorithm, Automatic White Balance (AWB) Algorithm, and Automatic Color Equalization (ACE) Algorithm. The algorithms were used on both static and dynamic water surface images, and the program results showed that the Automatic Color Equalization (ACE) Algorithm is the most appropriate image processing algorithm for water surface garbage cleaning robots.

Potato leaf disease detection with a novel deep learning model based on depthwise separable convolution and transformer networks

Early diagnosis of plant diseases is essential in reducing economic losses for farmers and increasing production efficiency. Therefore, Computer-Aided Diagnosis (CAD) systems supported by artificial intelligence technologies can be developed to help diagnose diseases quickly and accurately by examining the symptoms and signs in plant leaves. In this study, Multi-head Attention Mechanism Depthwise Separable Convolution Inception Reduction Network (MDSCIRNet) architecture, an image-based deep convolutional neural network, is proposed for classifying potato leaf diseases. The main components of the MDSCIRNet architecture are depthwise separable convolution (DSC) and a multi-head attention mechanism. The proposed architecture has been compared with modern algorithms developed with DSC technology, such as Xception, MobileNet, and deep learning algorithms, such as ResNet101, InceptionV3, and EfficientNetB2, to evaluate its performance in the classification process. In addition, hybrid methods developed with the classical machine learning algorithms Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Adaptive Boosting (AdaBoost), and MDSCIRNet model, integrated deep learning model, hard voting ensemble learning model. Suggested methods such as these were also used in the experimental process. Moreover, techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE), Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN), and Hypercolumn were used to improve the image quality of the data set. In the experimental process, the MDSCIRNet deep learning architecture achieved 99.24% accuracy in the study using the original dataset. While a 99.11% accuracy rate was achieved with the integrated deep learning model and hard voting ensemble learning model, the highest success rate of 99.33% was performed in the study conducted with the MDSCIRNet + SVM method. This study contributes to developing new and effective strategies in the agricultural industry for the early diagnosis and control of potato plant diseases. Machine learning-based approaches offer the potential to minimize economic losses and increase productivity in production by allowing farmers to intervene early.