Histogram Equalization Algorithm Research Articles

An Improved Underwater Visual SLAM through Image Enhancement and Sonar Fusion

To enhance the performance of visual SLAM in underwater environments, this paper presents an enhanced front-end method based on visual feature enhancement. The method comprises three modules aimed at optimizing and improving the matching capability of visual features from different perspectives. Firstly, to address issues related to insufficient underwater illumination and uneven distribution of artificial light sources, a brightness-consistency recovery method is proposed. This method employs an adaptive histogram equalization algorithm to balance the brightness of images. Secondly, a method for denoising underwater suspended particulates is introduced to filter out noise from images. After image-level processing, a combined underwater acousto–optic feature-association method is proposed, which associates acoustic features from sonar with visual features, thereby providing distance information for visual features. Finally, utilizing the AFRL dataset, the improved system incorporating the proposed enhancement methods is evaluated for its performance against the OKVIS framework. The system achieves a better trajectory estimation accuracy compared to OKVIS and demonstrates robustness in underwater environments.

Research on the Contrast Enhancement Algorithm for X-ray Images of BiFeO3 Material Experiment

High-Temperature Materials Science Experiment Cabinet on the Chinese Space Station is mainly used to carry out experimental research related to high-temperature materials science in microgravity. It is equipped with an X-ray transmission imaging module, which is applied to realize transmission imaging of material samples under microgravity. However, the X-ray light source is far away from the experimental samples, and the images obtained by the module are blurred, so it is impossible to accurately observe the morphological changes during the melting and solidification processes of high-temperature materials. To address this issue, this paper proposed a contrast enhancement algorithm specifically designed for X-ray images obtained during the experiments of high-temperature materials. The algorithm is based on gradient three-interval equalization, and it is combined with a Gaussian function to expand the gradient histogram. Meanwhile, the local gray level information within each gradient interval is corrected by designing an improved adaptive contrast enhancement algorithm. By comparing with Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithms, EnlightenGAN, and Wavelet algorithms, the Contrast Enhancement based contrast-changed Image Quality measure (CEIQ) and Measure of Enhancement (EME) are improved by an average of 56.97%, 10.58%, and Measure of Entropy (MOE) are improved by an average of 7.74 times. The experimental results show that the algorithm makes the image details clearer on the basis of image contrast enhancement. The solid-liquid interface in the image can be clearly observed after contrast enhancement. The algorithm provides strong support for the study of interface dynamics during the experiment process of high-temperature materials.

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.

Research on Underwater Image Enhancement Algorithm

In recent years, with the continuous expansion of human activity areas, the Marine pollution caused by human activities has become increasingly serious, and the large influx of agricultural emissions and household garbage needs to rely on underwater equipment to clean up. However, to identify garbage from the complex underwater environment, it is necessary to study the underwater image enhancement algorithm. Therefore, this paper takes underwater plastic floaters as the research object, starts with the optical imaging model of underwater images, analyzes the process and mechanism of underwater optical imaging, and its impact on image quality, so as to better understand the causes of image degradation. Based on white balance algorithm, histogram equalization algorithm, bilateral filter algorithm and gamma correction method, the underwater image recognition image is improved. The principle of these methods to enhance underwater image is analyzed and applied to further improve the effect of underwater image enhancement.

Effective classification of alzheimer disease based on image tractography framework utilizing GM-ABC-NN

A progressive neurological disorder that leads to memory loss as well as cognitive decline is called Alzheimer’s Disease (AD). Grounded on various factors like symptoms, biomarkers, or disease stages, the classification of AD is done. Yet, the prevailing techniques pose challenges in capturing accurate AD, which results in misdiagnosis or delayed treatment. To overcome these issues, an effective framework for the classification of AD based on image tractography using several algorithms is proposed in this article. Primarily, the dataset comprising AD images undergoes image transformation. Then, to enhance the MRI image quality, the transformed images are pre-processed using Block Matching 3D Filtering (BM3D) and Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithms. Afterward, image tractography followed by brain tissue segmentation is done utilizing the Cauchy-Steiner Weighted Fuzzy C Means (CauSt-FCM) algorithm. Then, by utilizing the Cosine Non-Negative Kernel Regression (CosNNK) algorithm, graph construction is done. Lastly, for the feature selection and validation process, the Premature Singer Archerfish Hunting Optimizer (PreS-AHO) algorithm and Analysis of Variance (ANOVA) analysis are used. Thereafter, the AD stages are classified by using the Gaussian Mixture Alpha-Beta Convolutional Neural Network (GM-ABC-NN) algorithm, The proposed system attains superior accuracy (97.56%), precision (97.55%), and recall (97.57%).

An Improved Multi Target Ship Recognition Model Based on Deep Convolutional Neural Network

Deep learning is the major technique used to identify objects in images captured by the synthetic aperture radar (SAR). While SAR images can be used to identify ships in general, detecting multiple ships or small vessels in these images in complex contexts remains an outstanding challenge. This study proposes a model of detection based on the improved PP-YOLO deep convolutional neural network that can identify multiple ships as well as small vessels in complex scenarios from SAR images. The histogram equalization algorithm is first used to preprocess the SAR images, and then the initial anchor box is optimized by using the shape similarity distance-based K-means clustering algorithm. Following this, the accuracy of the training network is improved based on the feature pyramid network and an attention mechanism. The experimental results show that the average accuracy (average precision) of the model was 94.25% at 41.63 frames per second on the GF-3 and the Sentinel-1 SAR datasets, superior to those of YOLOv3 (Darknet), YOLOv7, FPN (VGG), SSD, Faster R-CNN, and PP-YOLO (RestNet50-vd). The model also satisfies the demands of real-time detection.

Evaluating Recalibrating AI Models for Breast Cancer Diagnosis in a New Context: Insights from Transfer Learning, Image Enhancement and High-Quality Training Data Integration.

This paper investigates the adaptability of four state-of-the-art artificial intelligence (AI) models to the Australian mammographic context through transfer learning, explores the impact of image enhancement on model performance and analyses the relationship between AI outputs and histopathological features for clinical relevance and accuracy assessment. A total of 1712 screening mammograms (n = 856 cancer cases and n = 856 matched normal cases) were used in this study. The 856 cases with cancer lesions were annotated by two expert radiologists and the level of concordance between their annotations was used to establish two sets: a 'high-concordances subset' with 99% agreement of cancer location and an 'entire dataset' with all cases included. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of Globally aware Multiple Instance Classifier (GMIC), Global-Local Activation Maps (GLAM), I&H and End2End AI models, both in the pretrained and transfer learning modes, with and without applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. The four AI models with and without transfer learning in the high-concordance subset outperformed those in the entire dataset. Applying the CLAHE algorithm to mammograms improved the performance of the AI models. In the high-concordance subset with the transfer learning and CLAHE algorithm applied, the AUC of the GMIC model was highest (0.912), followed by the GLAM model (0.909), I&H (0.893) and End2End (0.875). There were significant differences (p < 0.05) in the performances of the four AI models between the high-concordance subset and the entire dataset. The AI models demonstrated significant differences in malignancy probability concerning different tumour size categories in mammograms. The performance of AI models was affected by several factors such as concordance classification, image enhancement and transfer learning. Mammograms with a strong concordance with radiologists' annotations, applying image enhancement and transfer learning could enhance the accuracy of AI models.

An efficient CNN-based detector for photovoltaic module cells defect detection in electroluminescence images

Electroluminescence (EL) imaging provides a high spatial resolution for inspecting photovoltaic (PV) cells, enabling the detection of various types of PV cell defects. Recently, convolutional neural network (CNN) based automatic detection methods for PV cell defects using EL images have attracted much attention. However, existing methods struggle to achieve a good balance between detection accuracy and efficiency. To address this issue, we propose a novel method for efficient PV cell defect detection. Firstly, we utilize Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm to improve EL image contrast, making defect features become more distinguishable. Secondly, we propose a lightweight defect detector using EfficientNet-B0 as its backbone. Moreover, we design a graph channel attention module (GCAM) to improve CNN’s limitation in modeling global information. It executes graph channel reasoning to generate enriched feature representation beyond the local receptive field, which is beneficial for distinguishing PV cell defects with similar local details. Next, we utilize focal loss to train the detector, enhancing its ability to detect challenging defects. Lastly, the proposed method is evaluated on the PVEL dataset and it achieved an accuracy of 97.81%, precision of 97.70%, recall of 97.59%, F1-score of 97.64%, and MCC of 97.32%, demonstrating our method is effective and outperforms state-of-the-art methods across various metrics.

Dual Histogram Equalization Algorithm Based on Adaptive Image Correction

For the visual measurement of moving arm holes in complex working conditions, a histogram equalization algorithm can be used to improve image contrast. To lessen the problems of image brightness shift, image over-enhancement, and gray-level merging that occur with the traditional histogram equalization algorithm, a dual histogram equalization algorithm based on adaptive image correction (AICHE) is proposed. To prevent luminance shifts from occurring during image equalization, the AICHE algorithm protects the average luminance of the input image by improving upon the Otsu algorithm, enabling it to split the histogram. Then, the AICHE algorithm uses the local grayscale correction algorithm to correct the grayscale to prevent the image over-enhancement and gray-level merging problems that arise with the traditional algorithm. It is experimentally verified that the AICHE algorithm can significantly improve the histogram segmentation effect and enhance the contrast and detail information while protecting the average brightness of the input image, and thus the image quality is significantly increased.

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.

Cervical Precancerous Lesion Image Enhancement Based on Retinex and Histogram Equalization

Cervical cancer is a prevalent chronic malignant tumor in gynecology, necessitating high-quality images of cervical precancerous lesions to enhance detection rates. Addressing the challenges of low contrast, uneven illumination, and indistinct lesion details in such images, this paper proposes an enhancement algorithm based on retinex and histogram equalization. First, the algorithm solves the color deviation problem by modifying the quantization formula of retinex theory. Then, the contrast-limited adaptive histogram equalization algorithm is selectively conducted on blue and green channels to avoid the problem of image visual quality reduction caused by drastic darkening of local dark areas. Next, a multi-scale detail enhancement algorithm is used to further sharpen the details. Finally, the problem of noise amplification and image distortion in the process of enhancement is alleviated by dynamic weighted fusion. The experimental results confirm the effectiveness of the proposed algorithm in optimizing brightness, enhancing contrast, sharpening details, and suppressing noise in cervical precancerous lesion images. The proposed algorithm has shown superior performance compared to other traditional methods based on objective indicators such as peak signal-to-noise ratio, detail-variance–background-variance, gray square mean deviation, contrast improvement index, and enhancement quality index.

Classification of the qilou (arcade building) using a robust image processing framework based on the Faster R-CNN with ResNet50

ABSTRACT Qilou (arcade building) is a particular type of Chinese historical architecture combined with western and eastern building elements, which plays a significant role in the history of modern Chinese architecture. However, the recognition and classification of the qilou mainly rely on manual inspection, suppressing the cultural dissemination and protection of qilou relics. In this paper, we present a new framework that adopts multiple image processing algorithms and a deep learning network to automate qilou classification. First, image dataset of the qilou is enhanced based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. Then, an improved Faster R-CNN with ResNet50 (Faster R-CNN-R) is deployed for qilou image recognition. A total of 760 images captured in Guangzhou were used for training, validation, and accuracy check of the proposed framework and several contrastive networks under the same conditions. Compared to other networks, the proposed framework works better than Faster R-CNN with VGG16 (Faster R-CNN-V) and FCOS. The accuracy of the proposed framework embedded with the Faster R-CNN-R, Faster R-CNN-V, and FCOS are 80.12%, 65.17%, and 66.35%, respectively. Based on digital images captured under different lighting conditions, the proposed framework can be used to classify nine different types of qilous, with high robustness.

Underwater image restoration based on adaptive parameter optimization of the physical model.

Underwater images have the advantage of carrying high information density and are widely used for marine information acquisition. Due to the complex underwater environment, the captured images are often unsatisfactory and often suffer from color distortion, low contrast, and blurred details. Physical model-based methods are often used in relevant studies to obtain clear underwater images; however, water selectively absorbs light, making the use of a priori knowledge-based methods no longer applicable and thus rendering the restoration of underwater images ineffective. Therefore, this paper proposes an underwater image restoration method based on adaptive parameter optimization of the physical model. Firstly, an adaptive color constancy algorithm is designed to estimate the background light value of underwater image, which effectively guarantees the color and brightness of underwater image. Secondly, aiming at the problem of halo and edge blur in underwater images, a smoothness and uniformity transmittance estimation algorithm is proposed to make the estimated transmittance smooth and uniform, and eliminate the halo and blur of the image. Then, in order to further smooth the edge and texture details of the underwater image, a transmittance optimization algorithm for smoothing edge and texture details is proposed to make the obtained scene transmittance more natural. Finally, combined with the underwater image imaging model and histogram equalization algorithm, the image blurring is eliminated and more image details are retained. The qualitative and quantitative evaluation on the underwater image dataset (UIEBD) shows that the proposed method has obvious advantages in color restoration, contrast and comprehensive effect, and has achieved remarkable results in application testing. It shows that the proposed method can effectively restore underwater degraded images and provide a theoretical basis for the construction of underwater imaging models.

Improved Preprocessing Strategy under Different Obscure Weather Conditions for Augmenting Automatic License Plate Recognition

Automatic license plate recognition (ALPR) systems are widely used for various applications, including traffic control, law enforcement, and toll collection. However, the performance of ALPR systems is often compromised in challenging weather and lighting conditions. This research aims to improve the effectiveness of ALPR systems in foggy, low-light, and rainy weather conditions using a hybrid preprocessing methodology. The research proposes the combination of dark channel prior (DCP), non-local means denoising (NMD) technique, and adaptive histogram equalization (AHE) algorithms in CIELAB color space. And used the Python programming language comparisons for SSIM and PSNR performance. The results showed that this hybrid approach is not merely robust to a variety of challenging conditions, including challenging weather and lighting conditions but significantly more accurate for existing ALPR systems.

IRCM-Caps: An X-ray image detection method for COVID-19.

COVID-19 is ravaging the world, but traditional reverse transcription-polymerase reaction (RT-PCR) tests are time-consuming and have a high false-negative rate and lack of medical equipment. Therefore, lung imaging screening methods are proposed to diagnose COVID-19 due to its fast test speed. Currently, the commonly used convolutional neural network (CNN) model requires a large number of datasets, and the accuracy of the basic capsule network for multiple classification is limital. For this reason, this paper proposes a novel model based on CNN and CapsNet. The proposed model integrates CNN and CapsNet. And attention mechanism module and multi-branch lightweight module are applied to enhance performance. Use the contrast adaptive histogram equalization (CLAHE) algorithm to preprocess the image to enhance image contrast. The preprocessed images are input into the network for training, and ReLU was used as the activation function to adjust the parameters to achieve the optimal. The test dataset includes 1200 X-ray images (400 COVID-19, 400 viral pneumonia, and 400 normal), and we replace CNN of VGG16, InceptionV3, Xception, Inception-Resnet-v2, ResNet50, DenseNet121, and MoblieNetV2 and integrate with CapsNet. Compared with CapsNet, this network improves 6.96%, 7.83%, 9.37%, 10.47%, and 10.38% in accuracy, area under the curve (AUC), recall, and F1 scores, respectively. In the binary classification experiment, compared with CapsNet, the accuracy, AUC, accuracy, recall rate, and F1 score were increased by 5.33%, 5.34%, 2.88%, 8.00%, and 5.56%, respectively. The proposed embedded the advantages of traditional convolutional neural network and capsule network and has a good classification effect on small COVID-19 X-ray image dataset.

Underwater Image Enhancement Based on the Improved Algorithm of Dark Channel

Enhancing underwater images presents a challenging problem owing to the influence of ocean currents, the refraction, absorption and scattering of light by suspended particles, and the weak illumination intensity. Recently, different methods have relied on the underwater image formation model and deep learning techniques to restore underwater images. However, they tend to degrade the underwater images, interfere with background clutter and miss the boundary details of blue regions. An improved image fusion and enhancement algorithm based on a prior dark channel is proposed in this paper based on graph theory. Image edge feature sharpening, and dark detail enhancement by homomorphism filtering in CIELab colour space are realized. In the RGB colour space, the multi-scale retinal with colour restoration (MSRCR) algorithm is used to improve colour deviation and enhance colour saturation. The contrast-limited adaptive histogram equalization (CLAHE) algorithm defogs and enhances image contrast. Finally, according to the dark channel images of the three processing results, the final enhanced image is obtained by the linear fusion of multiple images and channels. Experimental results demonstrate the effectiveness and practicality of the proposed method on various data sets.

Underwater Object Detection Using TC-YOLO with Attention Mechanisms.

Underwater object detection is a key technology in the development of intelligent underwater vehicles. Object detection faces unique challenges in underwater applications: blurry underwater images; small and dense targets; and limited computational capacity available on the deployed platforms. To improve the performance of underwater object detection, we proposed a new object detection approach that combines a new detection neural network called TC-YOLO, an image enhancement technique using an adaptive histogram equalization algorithm, and the optimal transport scheme for label assignment. The proposed TC-YOLO network was developed based on YOLOv5s. Transformer self-attention and coordinate attention were adopted in the backbone and neck of the new network, respectively, to enhance feature extraction for underwater objects. The application of optimal transport label assignment enables a significant reduction in the number of fuzzy boxes and improves the utilization of training data. Our tests using the RUIE2020 dataset and ablation experiments demonstrate that the proposed approach performs better than the original YOLOv5s and other similar networks for underwater object detection tasks; moreover, the size and computational cost of the proposed model remain small for underwater mobile applications.

Multi-view underwater image enhancement method via embedded fusion mechanism

Due to wavelength-dependent light absorption and scattering, underwater images often appear with a colour cast and blurry details. Most existing deep learning methods utilize a single input end-to-end network structure, which leads to a single form and content of the extracted features. To address these problems, we present a novel multi-feature underwater image enhancement method via embedded fusion mechanism (MFEF). We find that the quality of reconstruction results is affected by the quality of the input image to some extent, and use pre-processing to obtain high-quality images, which can improve the final reconstruction effect. We introduce the white balance (WB) algorithm and the contrast-limited adaptive histogram equalization (CLAHE) algorithm employing multiple path inputs to extract different forms of rich features in multiple views. To fully interact with features from multiple views, we design a multi-feature fusion (MFF) module to fuse derived image features. We suggest a novel pixel-weighted channel attention module (PCAM) that calibrates the detailed features of the degraded images using a weight matrix to give diverse weights to the encoded features. Ultimately, our network utilizes a fusion mechanism-based encoder and decoder that can be applied to restore various underwater scenes. In the UIEB dataset, our PSNR increased by 10.2% compared to that of Ucolor. Extensive experimental results demonstrate that the MFEF method outperforms other state-of-the-art underwater image enhancement methods in various real-world datasets.

Malware-on-the-Brain: Illuminating Malware Byte Codes With Images for Malware Classification

Malware is a piece of software that was written with the intent of doing harm to data, devices, or people. Since a number of new malware variants can be generated by reusing codes, malware attacks can be easily launched and thus become common in recent years, incurring huge losses in businesses, governments, financial institutes, health providers, etc. To defeat these attacks, malware classification is employed, which plays an essential role in anti-virus products. However, existing works that employ either static analysis or dynamic analysis have major weaknesses in complicated reverse engineering and time-consuming tasks. In this paper, we propose a visualized malware classification framework called VisMal, which provides highly efficient categorization with acceptable accuracy. VisMal converts malware samples into images and then applies a contrast-limited adaptive histogram equalization algorithm to enhance the similarity between malware image regions in the same family. We provided a proof-of-concept implementation and carried out an extensive evaluation to verify the performance of our framework. The evaluation results indicate that VisMal can classify a malware sample within 4.0 ms and have an average accuracy of 96.0%. Moreover, VisMal provides security engineers with a simple visualization approach to further validate its performance.

Feature extraction algorithm of an irregular small celestial body in a weak light environment.

This study focuses on creating a crater-matching algorithm to improve the matching rate and address the phenomenon of insufficient feature extraction and mismatching of irregular celestial objects and crater edge information on the dim surface of celestial bodies images. These images were captured by the detector's navigation camera. In order to improve the brightness and clarity of the images, the target images were filtered, denoised, and image-enhanced using the bilateral filtering method and improved histogram equalization algorithm, successively. Then, the enhanced image was extracted and matched using the ORB feature point detection algorithm based on scale invariance, and the feature point mismatch was processed by the Hamming distance screening method. The simulation results revealed that the optimization algorithm effectively improved the imaging quality of the target image in dark and weak light environments, increased the number of feature points extracted, reduced the mismatch of effective feature point pairs, and improved the matching rate.