Adaptive Contrast Enhancement Research Articles

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.

Fusing Deep Immuno Net and Random Forest to Improve the Detection of Dengue Fever: A Computational Strategy for Prompt and Precise Diagnosis

Dengue fever is a worldwide health issue caused by the virus. Effective medical management and avoidance of severe consequences need fast and precise Dengue detection. This research uses advanced image pre-processing, deep learning methods using convolutional neural networks (CNNs), and feature selection to improve Dengue diagnosis. Our first study focuses on improving input images using cutting-edge image enhancement methods, including Adaptive Contrast Enhancement using Histogram Equalization (ACE-HE)during pre-processing. This improves visual data for processing, enabling Dengue identification. Feature extraction, using a CNN architecture optimized for Dengue detection, is our core technique. We also use current neural network designs like EfficientNet and transformers to extract subtle characteristics needed for effective diagnosis. Our approach uses computational evolutionary algorithms and neural structure searches to identify the most relevant features from the large pool of retrieved information. These characteristics are gradually fused to provide a complete depiction of Dengue's complicated patterns. Dengue detection is completed using high-performance classifiers including Random Forest variations and ensemble approaches. Our architecture is adaptable and resilient, achieving above 97.92% BCCD dataset classification accuracy. This technology represents a major advance in Dengue diagnosis and meets the worldwide requirement for rapid and accurate infectious illness identification. Our strategy uses the latest computer vision and neural network technology to produce dependable and effective tools for rapid and accurate Dengue diagnosis, addressing a crucial worldwide healthcare issue.

Adaptive contrast enhancement for underwater image using imaging model guided variational framework

Adaptive contrast enhancement for underwater image using imaging model guided variational framework

MRI brain tumor detection using deep learning and machine learning approaches

The development of aberrant brain cells, some of which may become cancerous, is known as a brain tumour. The quality of life and life expectancy of patients are enhanced by early and timely illness identification and treatment plans. Magnetic Resonance Imaging (MRI) scans are the most common approach for finding brain tumors. However, the ability of radiologists and other clinical experts to identify, segment, and remove contaminated tumour regions from MRI images is a critical factor in a process that is iterative and labor-intensive and relies on those individuals' abilities in these areas. Concepts for image processing may envision the diverse human organ anatomical structures. It is difficult to find abnormal brain regions using simple imaging methods. Over the last several years, interest in the emerging machine learning field of “Deep Learning (DL)" has grown significantly. It was extensively used in numerous applications and shown to be an effective Machine Learning (ML) technique for many of the challenging issues. This research suggests a novel MRI brain tumour detection method based on DL and ML. Initially the MRI images are collected and preprocessed using Adaptive Contrast Enhancement Algorithm (ACEA) and median filter. Fuzzy c-means based segmentation is done to segment the preprocessed images. The features like energy, mean, entropy and contrast are extracted using Gray-level co-occurrence matrix (GLCM). The abnormal tissues are classified using the proposed Ensemble Deep Neural Support Vector Machine (EDN-SVM) classifier. The numerical findings reveal a better accuracy (97.93 %), sensitivity (92 %), and specificity (98 %) in recognizing aberrant and normal tissue from brain MRIimages, which supports the effectiveness of the approach that was recommended.

PREVENTION OF RODENT ATTACK ON TERTIARY PACKAGE AT STORAGE LEVEL

This proposes to use some image processing methods as a data normalization method for machine learning. Conventionally, z-score normalization is widely used for pre-processing of data. In the proposed approach, in addition to z-score normalization, a number of histogram-based image processing methods such as histogram equalization are applied to training data and test data as a pre-processing method for machine learning. We evaluate the effectiveness of the proposed approach by using a support vector machine algorithm and a random forest one. In experiments, the proposed scheme is applied to a face-based authentication algorithm with SVM/random forest classifiers to confirm the effectiveness. For SVM classifiers, both z- score normalization and image enhancement work well as a pre-processing method for improving the accuracy. In contrast, for random forest classifiers, a number of image enhancement methods work well, although z- score normalization is unusual for improving the accuracy. The proposed pre-processing method focuses on leveraging histogram analysis to address challenges related to contrast variations, colour distributions, and noise levels in diverse image datasets. The key objectives include the development of an adaptive contrast enhancement algorithm, colour normalization techniques, and strategies for noise reduction.

UIE-FSMC: Underwater Image Enhancement Based on Few-Shot Learning and Multi-Color Space

Light propagates in water with certain attenuation, which causes quality problems, such as color cast, low contrast, and low illumination, in underwater images. Moreover, it is generally difficult to obtain a large number of real-world underwater images and the corresponding ground truth in convoluted underwater environments. To address these two problems, this article recommends an underwater image enhancement scheme based on few-shot learning and multi-color space, called UIE-FSMC. Specifically, for the first time, we propose a lightweight underwater image enhancement network based on few-shot learning. We design a new strategy to train the network. Specific training steps include the following: First, synthetic underwater images are used for large-scale pre-training, which can obtain the initial weight of the network. Then, according to the characteristics of the data, meta-learning based on supervised and unsupervised loss is suggested. It further enhances the feature expression aptitude of the network by learning the external and internal characteristics of the data. Finally, fine-tuning based on supervised and unsupervised loss is designed. It further improves the accuracy, robustness, and generalization of the network through an average strategy. In addition, we design a post-processing method founded on the RGB and LAB color spaces. In the RGB color space, we suggest a local region-based adaptive color correction method for underwater images. In the LAB color space, we design a multi-scale local adaptive contrast enhancement method for the L channel and a local region-based color balance strategy for the AB channels. Quantitative and qualitative experiments on five different underwater image datasets show that the outcomes of UIE-FSMC are superior to those of other techniques. Furthermore, application research further validates the excellent performance of UIE-FSMC.

Image enhancement using an improved adaptive contrast enhancement algorithm in neutron radiography

As a non-destructive testing technique, neutron radiography has received increasing attention in scientific research. However, the inherent limitations of neutron images, such as insufficient resolution and contrast, have been attributed to the low conversion efficiency of the neutron conversion screen and the low-light quantum efficiency of the charge-coupled device camera. This paper presents a novel neutron image enhancement method that utilizes the sparrow search algorithm to automatically optimize the gain factor coefficient of the adaptive contrast enhancement algorithm and incorporates neutron image information to achieve image enhancement. Experimental results show that compared with other enhancement methods, the proposed method can effectively improve the quality of neutron images visually and numerically and has good operational stability.

Adaptive rule-based colour component weight assignment strategy for underwater video enhancement

ABSTRACT Images and videos collected in an underwater environment often have low contrast, blur, and colour cast due to two significant sources of distortion; light scattering and absorption. In an underwater image/video, suspended particles attenuate red and blue components more than green channels. This article presents two adaptive weight allocation strategies based on rule assignment for red, green, and blue channels. Firstly, an improved balanced contrast enhancement technique (IBCET) is proposed using an adaptive contrast enhancement scheme based on colour component weight assignment. Secondly, a modified fuzzy contrast enhancement technique which obtains the intensification factor based on the weight of each component is developed. Finally, principal component analysis fusion is employed to improve the overall colour and contrast of the output video frames. Qualitative and quantitative evaluations on the standard underwater video dataset are demonstrated to validate the improved performance of the proposed algorithm.

Hybrid algorithm for the detection of turbulent flame fronts

This paper presents a hybrid and unsupervised approach to flame front detection for low signal-to-noise planar laser-induced fluorescence (PLIF) images. The algorithm combines segmentation and edge detection techniques to achieve low-cost and accurate flame front detection in the presence of noise and variability in the flame structure. The method first uses an adaptive contrast enhancement scheme to improve the quality of the image prior to segmentation. The general shape of the flame front is then highlighted using segmentation, while the edge detection method is used to refine the results and highlight the flame front more accurately. The performance of the algorithm is tested on a dataset of high-speed PLIF images and is shown to achieve high accuracy in finely wrinkled turbulent hydrogen-enriched flames with order of magnitude improvements in computation speed. This new algorithm has potential applications in the experimental study of turbulent flames subject to intense wrinkling and low signal-to-noise ratios.Graphic abstract

Design of Low Vision Electronic Glasses with Image Processing Capabilities Using Raspberry Pi

Poor vision is one of the most common eye health issues worldwide. Low vision patients are typically treated with optical devices or by substituting hearing or touch for visual capabilities. Head-mounted displays are currently the most promising form of low-vision assistive technology since they utilize the user's remaining natural visual capabilities. In this work, a prototype head-mounted display-based low-vision tool in the form of electronic glasses was designed utilizing a Raspberry Pi computer. The prototype was created using a Raspberry Pi 4 B coupled with cameras to allow real-time video acquisition. The LCD on the electronic eyewear frame as the camera showed the video recording. The prototype also included software utilizing five image processing modes—magnification, brightness enhancement, adaptive contrast enhancement, edge enhancement, and text detection and recognition- to help persons with limited vision acquire visual information more effectively. OpenCV was used with Python to create the software system. Average framerate measurements of 30–40 FPS for brightness and contrast improvement modes, 20 FPS for zooming and edge enhancement modes, and 1.3 FPS for text identification modes showed that the concept of electronic spectacles was successfully implemented in this research.

An Improved YOLOv5-Based Underwater Object-Detection Framework

To date, general-purpose object-detection methods have achieved a great deal. However, challenges such as degraded image quality, complex backgrounds, and the detection of marine organisms at different scales arise when identifying underwater organisms. To solve such problems and further improve the accuracy of relevant models, this study proposes a marine biological object-detection architecture based on an improved YOLOv5 framework. First, the backbone framework of Real-Time Models for object Detection (RTMDet) is introduced. The core module, Cross-Stage Partial Layer (CSPLayer), includes a large convolution kernel, which allows the detection network to precisely capture contextual information more comprehensively. Furthermore, a common convolution layer is added to the stem layer, to extract more valuable information from the images efficiently. Then, the BoT3 module with the multi-head self-attention (MHSA) mechanism is added into the neck module of YOLOv5, such that the detection network has a better effect in scenes with dense targets and the detection accuracy is further improved. The introduction of the BoT3 module represents a key innovation of this paper. Finally, union dataset augmentation (UDA) is performed on the training set using the Minimal Color Loss and Locally Adaptive Contrast Enhancement (MLLE) image augmentation method, and the result is used as the input to the improved YOLOv5 framework. Experiments on the underwater datasets URPC2019 and URPC2020 show that the proposed framework not only alleviates the interference of underwater image degradation, but also makes the mAP@0.5 reach 79.8% and 79.4% and improves the mAP@0.5 by 3.8% and 1.1%, respectively, when compared with the original YOLOv8 on URPC2019 and URPC2020, demonstrating that the proposed framework presents superior performance for the high-precision detection of marine organisms.

Citrus fruit content and disease detection using adaptive contrast enhancement

With the advent of modernized materials and automation-based methods almost each and every field is progressing towards application of software based methods. In this article an approach using modernized software method based on content and material analysis for detection of diseases in citrus fruits is proposed. The major focus for discussion is design of pre-processing stage which extract the details present on the surface of the fruit defining the material level details. This will ensure the improvement in quality of images so as to make the whole process efficient. The algorithm proposed through this paper combines two methodologies Un-sharp masking and Genetic algorithm which aims toward improvising the contrast thereby resulting in image enhancement, food production, agricultural productivity and food gap. The proposed algorithm applied on surface level details is tested for different types of citrus diseases. Experimental results obtained through simulation on different diseased images using peak signal-to-noise ratio, structural similarity index metric, Blind Image Spatial Quality Evaluator and show that the proposed algorithm outperforms other methods for quality enhancement of images both subjectively and objectively.

Underwater optical image enhancement based on super-resolution convolutional neural network and perceptual fusion.

Underwater optical images often have serious quality degradations and distortions, which hinders the development of underwater optics and vision systems. Currently, there are two mainstream solutions: non-learning based and learning-based. Both have their advantages and disadvantages. To fully integrate the advantages of both, we propose an enhancement method based on superresolution convolutional neural network (SRCNN) and perceptual fusion. First, we introduce a weighted fusion BL estimation model with a saturation correction factor (SCF-BLs fusion), the accuracy of image prior information is improved effectively. Next, a refined underwater dark channel prior (RUDCP) is proposed, which combines guided filtering and an adaptive reverse saturation map (ARSM) to restore the image, which not only preserves edge details but also avoids the interference of artificial light. Then, the SRCNN fusion adaptive contrast enhancement is proposed to enhance the colour and contrast. Finally, to further enhance image quality, we employ efficient perceptual fusion to blend the different resulting outputs. Extensive experiments demonstrate that our method has outstanding visual results in underwater optical image dehazing, color enhancement and is artefact- and halo-free.

Multi-temporal satellite image correction based on CNN features and adaptive contrast enhancement

In recent years, identifying changes in multi-temporal images in terms of land use and land cover has been significant in a variety of applications, including urban planning. Due to weather and environmental effects, optical remote sensing has limitations in obtaining images where the image quality may be degraded. It’s because the images being registered are taken at various times, viewpoints, and types of sensors. In this article, the pre-processing methods, which include radiometric correction and geometric correction, are introduced to enhance the quality of satellite images and identify correct spatial alignment. For radiometric correction, adaptive contrast enhancement is done by combining histogram- and non-linear transfer function-based approaches in CIELAB color space. A comparison study is done to see how the new method compared to other methods. For geometric correction, the features from two images are extracted using Convolutional Neural Network to match and align them. The introduced approach for radiometric correction gave the best average rank of BRISQUE scores and RMSE of contrast scores, and the geometric correction can align two images together with an average accuracy of improvement of 91.78 percent. The findings of this research will provide the preliminary step for any change detection activities.

47‐1: Display Outdoor Visibility Enhancement Using Adaptive Tone Mapping

In this document, we propose an image tone mapping method enhancing the image visibility in outdoor environment. The proposed method improves the outdoor visibility of the displayed image in two aspects: brightness enhancement and content adaptive contrast enhancement. From the experiment results, it is confirmed that the proposed method improves the brightness and the contrast at the same time and enhances outdoor visibility effectively even with the limited dynamic range of the display device. Compared to conventional methods, proposed method has higher flexibility and effectiveness. This method is already utilized on the mobile devices for outdoor visibility enhancement and we expect to apply it to the automotive platforms.

Low-Light Image Enhancement Method Based on Retinex Theory by Improving Illumination Map

Recently, low-light image enhancement has attracted much attention. However, some problems still exist. For instance, sometimes dark regions are not fully improved, but bright regions near the light source or auxiliary light source are overexposed. To address these problems, a retinex based method that strengthens the illumination map is proposed, which utilizes a brightness enhancement function (BEF) that is a weighted sum of the Sigmoid function cascading by Gamma correction (GC) and Sine function, and an improved adaptive contrast enhancement (IACE) to enhance the estimated illumination map through multi-scale fusion. Specifically, firstly, the illumination map is obtained according to retinex theory via the weighted sum method, which considers neighborhood information. Then, the Gaussian Laplacian pyramid is used to fuse two input images that are derived by BEF and IACE, so that it can improve brightness and contrast of the illuminance component acquired above. Finally, the adjusted illuminance map is multiplied by the reflection map to obtain the enhanced image according to the retinex theory. Extensive experiments show that our method has better results in subjective vision and quantitative index evaluation compared with other state-of-the-art methods.

An Improved Infrared and Visible Image Fusion Using an Adaptive Contrast Enhancement Method and Deep Learning Network with Transfer Learning

Deep learning (DL) has achieved significant attention in the field of infrared (IR) and visible (VI) image fusion, and several attempts have been made to enhance the quality of the final fused image. It produces better results than conventional methods; however, the captured image cannot acquire useful information due to environments with poor lighting, fog, dense smoke, haze, and the noise generated by sensors. This paper proposes an adaptive fuzzy-based preprocessing enhancement method that automatically enhances the contrast of images with adaptive parameter calculation. The enhanced images are then decomposed into base and detail layers by anisotropic diffusion-based edge-preserving filters that remove noise while smoothing the edges. The detailed parts are fed into four convolutional layers of the VGG-19 network through transfer learning to extract features maps. These features maps are fused by multiple fusion strategies to obtain the final fused detailed layer. The base parts are fused by the PCA method to preserve the energy information. Experimental results reveal that our proposed method achieves state-of-the-art performance compared with existing fusion methods in a subjective evaluation through the visual experience of experts and statistical tests. Moreover, the objective assessment parameters are conducted by various parameters (FMI, SSIMa, API, EN, QFAB, and NFAB) which were used in the comparison method. The proposed method achieves 0.2651 to 0.3951, 0.5827 to 0.8469, 56.3710 to 71.9081, 4.0117 to 7.9907, and 0.6538 to 0.8727 gain for FMI, SSIMa, API, EN, and QFAB, respectively. At the same time, the proposed method has more noise reduction (0.3049 to 0.0021) that further justifies the efficacy of the proposed method than conventional methods.

Underwater Image Enhancement via Minimal Color Loss and Locally Adaptive Contrast Enhancement.

Underwater images typically suffer from color deviations and low visibility due to the wavelength-dependent light absorption and scattering. To deal with these degradation issues, we propose an efficient and robust underwater image enhancement method, called MLLE. Specifically, we first locally adjust the color and details of an input image according to a minimum color loss principle and a maximum attenuation map-guided fusion strategy. Afterward, we employ the integral and squared integral maps to compute the mean and variance of local image blocks, which are used to adaptively adjust the contrast of the input image. Meanwhile, a color balance strategy is introduced to balance the color differences between channel a and channel b in the CIELAB color space. Our enhanced results are characterized by vivid color, improved contrast, and enhanced details. Extensive experiments on three underwater image enhancement datasets demonstrate that our method outperforms the state-of-the-art methods. Our method is also appealing in its fast processing speed within 1s for processing an image of size 1024×1024×3 on a single CPU. Experiments further suggest that our method can effectively improve the performance of underwater image segmentation, keypoint detection, and saliency detection. The project page is available at https://li-chongyi.github.io/proj_MMLE.html.

Adaptive Image Enhancement Method Based on Gamma Correction

To address the problems of local detail loss and sharpness degradation in traditional image enhancement algorithms under low illumination conditions, an image enhancement algorithm based on the Weber-Fechner law is proposed. The adaptive gamma correction function and the adaptive contrast enhancement function are used to reduce the noise interference of the original image, the RGB mode of the original image is converted to HSV mode to improve the overall visual comfort of the image, the classical adaptive correction algorithm is optimised to separate the luminance components into blocks and obtain two images; Finally, the image fusion technique is used to extract the details from the two images and synthesise the final image. The images enhanced are clearer, brighter and more natural than the classical algorithms.

Image Adaptive Contrast Enhancement for Low-illumination Lane Lines Based on Improved Retinex and Guided Filter

ABSTRACT In a low-illumination environment, the contrast between lane lines and the ground is relatively low. Traditional image enhancement algorithms, such as gamma correction, Histogram Equalization, and multiple-scale Retinex, may result in over enhancement and detail loss, which decreases the detection accuracy of driver assistance systems. In this work, we introduce a low-illumination image enhancement algorithm based on improved Retinex theory and apply it to lane-line detection. A luminance channel optimization method based on a bimodal energy function is adopted to select the weight of the linear combination. A guided filter with edge-preservation is applied to obtain the illumination component of the scene. Furthermore, the reflection component is estimated in a hyperbolic tangent space, and the luminance and contrast are adaptively adjusted to obtain the enhanced image. Experimental results show that proposed method can effectively extract the lane-line edges and suppress the noise in the dark area with low lane-line illumination. Moreover, it can improve the detection success rate of an assisted driving system.