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The incorporation of impurity elements into minerals impacts their physical properties (e.g., reflectance, hardness, and electrical conductivity), but the quantitative relationships between these features and compositional variation remain inadequately constrained. Prior work has shown that gamma-enhancement of reflected light images represents a simple yet powerful tool to assess the compositional heterogeneity of single pyrite crystals, as it can enhance subtle differences in reflectance between distinct domains with different minor element concentrations. This study extends the gamma correction method to several other common Fe-bearing minerals, magnetite, garnet, wolframite, and tetrahedrite-tennantite, which all have far lower reflectance than pyrite. Gamma-enhanced optical images reveal clear variations in reflectance that are either systematic with increased minor element concentration, as the change in gray value on backscatter electron (BSE) images (in the case of magnetite, garnet, and tetrahedrite-tennantite) or contrasting (as in pyrite), yielding a convincing linkage between reflectance variation and compositional heterogeneity. Reflectance variation is an expression of the distribution of the average effective number of free electrons on the mineral surface that can re-emit light when excited by visible light. Gamma-enhanced images can reveal compositional heterogeneity in minerals such as wolframite where small atomic mass differences between substituting elements (Mn and Fe, in the case of wolframite) are virtually impossible to observe as a variation of gray values on BSE images. Results also demonstrate that Fe-rich domains in these minerals can be expected to have higher reflectance than Fe-poor domains whenever Fe is a major constituent. The greater reflectance is attributed to Fe ions having a greater effective number of free electrons than many other elements (e.g., Co, Ni, Si, Ca, Al, Mg, Mn, and As). This research highlights the utility of gamma correction as an inexpensive tool for routine evaluation of compositional heterogeneity in common Fe-bearing minerals, potentially obviating the necessity of a microbeam platform to correlate textures and composition. [ABSTRACT FROM AUTHOR]

Breast cancer remains a critical global health concern, given the crucial role of early detection in achieving successful treatment outcomes. By harnessing the power of deep learning, our proposed methodology aims to discern intricate patterns and nuances in breast tissue textures, enabling robust discrimination between benign and malignant tumors. We start to search for solutions using two directions: an intelligent system that uses Convolutional Neural Networks (CNNs) over the images and another model that uses CNNs over textural features extracted from mammograms. This research comes as an extension to our previous work on the Classification of mammograms into benign and malignant types using textural features and shallow classifiers. While these methods provided valuable insights, we sought to explore the future of CNNs in increasing the accuracy of breast cancer detection. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Underwater images often exhibit color deviation, reduced contrast, distortion, and other issues due to light refraction, scattering, and absorption. Therefore, restoring detailed information in underwater images and obtaining high-quality results are primary objectives in underwater image enhancement tasks. Recently, deep learning-based methods have shown promising results, but handling details in low-light underwater image processing remains challenging. In this paper, we propose an attention-based color consistency underwater image enhancement network. The method consists of three components: illumination detail network, balance stretch module, and prediction learning module. The illumination detail network is responsible for generating the texture structure and detail information of the image. We introduce a novel color restoration module to better match color and content feature information, maintaining color consistency. The balance stretch module compensates using pixel mean and maximum values, adaptively adjusting color distribution. Finally, the prediction learning module facilitates context feature interaction to obtain a reliable and effective underwater enhancement model. Experiments conducted on three real underwater datasets demonstrate that our approach produces more natural enhanced images, performing well compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Diabetes mellitus persons have an eye disease that is called Diabetic Retinopathy (DR). The early discovery of this disease is very useful to determine its degree. Fundus images can be used for the detection of DR, according to symptoms that are apparent in the retina. In this paper, a simple method based on Dynamic Piecewise Linear Transformation (DPLT) and gamma correction is proposed to improve the quality of retinal images that can be used for subsequent diagnosis. To evaluate the results of this method, some images with low contrast and poor details are used in the test experiments. The lightness order error and entropy are used as quantitative metrics to evaluate the results. The simulation results prove that this method gives more accurate and better-visual-quality retinal images. [ABSTRACT FROM AUTHOR]

Pavement cracks are a kind of common distress in road service time, and their length measurement is critical for pavement maintenance. The current automatic method of crack length measurement uses segmentation algorithms to obtain crack curves, which is time-consuming and complex. In this study, an effective method of crack length measurement was proposed and validated. The method consists of a detection module based on an object detection algorithm and a length calculation module. To increase the speed and accuracy of crack detection, an improved pavement crack detection algorithm BiFPN-enhanced YOLO V5 (YOLO V5-BiFPN) based on you look only once version 5 (YOLO V5) and bidirectional feature pyramid network (BiFPN) is proposed, and gamma correction was utilized to process pavement images. YOLO V5-BiFPN was tested in a real pavement image data set and achieved remarkable performance. In the length calculation module, the diagonal length of the crack bounding box output by the object detection algorithm can be defined as the crack length. To validate the measurement method, the true value of crack length was obtained from the segmentation data set by skeletonization. The error between the calculation result of the proposed method and the real value is 3.4%, and the average processing time of each image is 14.2 ms. The developed method addresses the problem of considerable time and financial cost associated with the existing crack length measurement methods. [ABSTRACT FROM AUTHOR]

The weather has a detrimental effect on outdoor vision systems and raises the probability of traffic crashes and road accidents. The scattering of atmospheric particles degrades outdoor images captured in poor weather conditions such as haze and fog. The reduced visibility has a significant impact on driving assistance systems designed for automatic vehicles. As a result, clear visibility is critical for outdoor computer vision systems. Image dehazing is one of the ill-posed problems because evaluating transmission depth is challenging. It is essential to estimate transmission depth with the greatest degree of accuracy. In order to estimate or optimize the transmission depth, this paper employs the adaptive Gaussian notch filter and the concept of gamma correction to recover the final scene radiance. The results of the experiments are assessed and compared both quantitatively and qualitatively with state-of-the-art techniques. The experimental results demonstrate that the proposed indicators ensure high consistency in qualitative and quantitative evaluation using six performance metrics: two blind assessment indicators (e, r), contrast gain (C gain) , visual contrast measure (VCM), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and probability. [ABSTRACT FROM AUTHOR]

Introduction: Brain tumors are a common disease that affects millions of people worldwide. Considering the severity of brain tumors (BT), it is important to diagnose the disease in its early stages. With advancements in the diagnostic process, Magnetic Resonance Imaging (MRI) has been extensively used in disease detection. However, the accurate identification of BT is a complex task, and conventional techniques are not sufficiently robust to localize and extract tumors in MRI images. Therefore, in this study, we used a deep learning model combined with a segmentation algorithm to localize and extract tumors from MR images. Method: This paper presents a Deep Learning (DL)-based You Look Only Once (YOLOv7) model in combination with the Grab Cut algorithm to extract the foreground of the tumor image to enhance the detection process. YOLOv7 is used to localize the tumor region, and the Grab Cut algorithm is used to extract the tumor from the localized region. Results: The performance of the YOLOv7 model with and without the Grab Cut algorithm is evaluated. The results show that the proposed approach outperforms other techniques, such as hybrid CNN-SVM, YOLOv5, and YOLOv6, in terms of accuracy, precision, recall, specificity, and F1 score. Discussion: Our results show that the proposed technique achieves a high dice score between tumor-extracted images and ground truth images. The findings show that the performance of the YOLOv7 model is improved by the inclusion of the Grab Cut algorithm compared to the performance of the model without the algorithm. [ABSTRACT FROM AUTHOR]

Aiming to address the problems of uneven brightness and small defects of low contrast on the surface of lithium-ion battery electrode (LIBE) coatings, this study proposes a defect detection method that combines background reconstruction with an enhanced Canny algorithm. Firstly, we acquire and pre-process the electrode coating image, considering the characteristics of the electrode coating process and defects. Secondly, background reconstruction and the difference method are introduced to achieve the rough localization of coating defects. Furthermore, the image with potential defects undergoes enhancement through improved Gamma correction, and the PSO-OTSU algorithm with adaptive searching is applied to determine the optimal segmentation. Finally, precise defect detection is accomplished using the improved Canny algorithm and morphological processing. The experimental results show that, compared with the maximum entropy method, the region growth method, and the traditional Canny algorithm, the algorithm in this paper has a higher segmentation accuracy for defects. It better retains defect edge features and provides a more accurate detection effect for defects like scratches, dark spots, bright spots, metal leakage, and decarburization, which are difficult to recognize on the background of coating areas of electrodes. The proposed method is suitable for the online real-time defect detection of LIBE coating defects in actual lithium-ion battery industrial production. [ABSTRACT FROM AUTHOR]

The space of the fully mechanized working face is small, and the lighting environment is complex and variable. During the coal mining process, there is a large amount of dust and fog, which leads to problems such as exposure and weakened detail features in the collected images. It is difficult to effectively extract features from images with excessive lighting intensity in the underground lighting area. In order to solve the above problems, a denoising algorithm for uneven lighting images in coal mines is proposed. Firstly, the video is captured as an image to determine whether lighting suppression is necessary. The RGB image that requires lighting suppression is split into channels, and the lighting adjustment factor for each channel is calculated to achieve overall lighting adjustment of the image. Secondly, the images that have not undergone overall lighting suppression and those that have undergone overall lighting suppression are subjected to reflection component extraction. The input image is converted into an HSV spatial image, and the single scale Retinex (SSR) algorithm is used to separately process the illumination component in the V channel image. The incident component in the V component is removed, while the reflection component is retained. The histogram equalization algorithm is used to achieve illumination equalization for the reflection component. Finally, a dark channel prior algorithm with guided filtering is used to defog the light-processed image. The gamma correction function is used to readjust the image with uneven brightness. The subjective evaluation results indicate that the proposed denoising algorithm for uneven lighting images in coal mines effectively suppresses the problem of high overall brightness caused by lighting. The blurry parts of the original image are clearer due to factors such as fog and dust, and the detailed features of the image are more prominent. The effectiveness of the proposed algorithm is objectively evaluated using four evaluation indicators: information entropy, mean, standard deviation, and spatial frequency. The results showed that the proposed algorithm has achieved an average improvement of 21.87%, −56.06%, 153.43%, and 294.45% in terms of information entropy, mean, standard deviation, and spatial frequency compared to the multi-scale Retinex (MSR) algorithm. The proposed algorithm has achieved an average improvement of 1.18%, −39.56%, 33.29%, and −4.71% compared to the multi-scale Retinex with color preservation (MSRCR) algorithm. The proposed algorithm has achieved an average improvement of 38.06%, −55.27%, 462.10%, and 300.96% compared to the multi-scale Retinex with color restoration (MSRCR) algorithm. The results indicate that the proposed algorithm can more effectively increase image information, suppress lighting intensity, improve edge information, and image clarity.

Medical imaging enables doctors to provide faster and more accurate diagnoses of different conditions. Medical images are generally very dark and frequently exhibit degradations such as poor detail or low contrast, which may impact the accuracy and speed of diagnosis. This paper proposes an effective and efficient technique to enhance CT images. The image is first decomposed into base and detail layers, which are individually enhanced using adaptive gamma correction, and recombined to obtain the resultant image with better details and brightness without added noise and artifacts such as halo. We present a comprehensive study using 51 test images evaluated by 50 human subjects to compare the performance of the proposed method with the existing state of the art. In addition, we use six commonly used objective metrics to score the images produced by the proposed method and seven existing state of the art enhancement methods. The proposed method outperforms the existing techniques in both objective and subjective evaluations and appears as the most effective way of enhancing medical images’ quality without producing artifacts.

In order to obtain accurate information from low illumination images, we propose a low-light image enhancement method based on guided filter and Gamma correction according to the Retinex model. First, the image is converted into the Lab color space. After that, the guided filter is used to extract the scene's illumination and the parameter of local square window radius is updated by the size of source image. Then, a novel adaptive Gamma correction, based on heat transfer law, is applied to achieve precise illumination intensity. Finally, the illumination component with color information is to obtain the reconstructed enhancement image. The ablation analysis indicates the effectiveness of main part in the proposed method. Through numerous experiments, the proposed method enhances the overall brightness, corrects the color distortion, preserves details, and demonstrates favorable visual results for diverse low-light images. The proposed method also shows certain superiority and comparability in objective and subjective evaluations compared to state-of-the-art methods, and meanwhile remains highly efficient. [ABSTRACT FROM AUTHOR]

Haze is the natural phenomenon, which affects an image's air light and visibility. It creates a layer that hides the information in an acquired hazy image and decreases its visibility. Hazy scenarios are mostly seen in the transportation sector and remote sensing. It affects the quality of an image captured. Haze is one of the major hurdles in several computer vision applications. This paper observes and analyses different methods of haze removal via image enhancement techniques. Proposes the weighted average of the image enhancement methods to generate the enhanced hazy input image as the initial step. These enhanced images do train the neural network to estimate transmission map as well as atmospheric light, used for haze removal from images. The proposed method is experimented with 135 hazy images from three standard datasets, alias I-Haze, NH-Haze, and O-Haze (45 images from each total 135 hazy images). It gives clearer results than a few similar existing haze removal techniques. Also, the experimental results tested with performance metrics Entropy PSNR, and SSIM have demonstrated the effectiveness of the proposed haze removal method having weighted fusion of pre-processing techniques. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

IntroductionBrain tumors are a common disease that affects millions of people worldwide. Considering the severity of brain tumors (BT), it is important to diagnose the disease in its early stages. With advancements in the diagnostic process, Magnetic Resonance Imaging (MRI) has been extensively used in disease detection. However, the accurate identification of BT is a complex task, and conventional techniques are not sufficiently robust to localize and extract tumors in MRI images. Therefore, in this study, we used a deep learning model combined with a segmentation algorithm to localize and extract tumors from MR images.MethodThis paper presents a Deep Learning (DL)-based You Look Only Once (YOLOv7) model in combination with the Grab Cut algorithm to extract the foreground of the tumor image to enhance the detection process. YOLOv7 is used to localize the tumor region, and the Grab Cut algorithm is used to extract the tumor from the localized region.ResultsThe performance of the YOLOv7 model with and without the Grab Cut algorithm is evaluated. The results show that the proposed approach outperforms other techniques, such as hybrid CNN-SVM, YOLOv5, and YOLOv6, in terms of accuracy, precision, recall, specificity, and F1 score.DiscussionOur results show that the proposed technique achieves a high dice score between tumor-extracted images and ground truth images. The findings show that the performance of the YOLOv7 model is improved by the inclusion of the Grab Cut algorithm compared to the performance of the model without the algorithm.

From literature, majority of face recognition modules suffer performance challenges when presented with test images acquired under multiple constrained environments (occlusion and varying expressions). The performance of these models further deteriorates as the degree of degradation of the test images increases (relatively higher occlusion level). Deep learning-based face recognition models have attracted much attention in the research community as they are purported to outperform the classical PCA-based methods. Unfortunately their application to real-life problems is limited because of their intensive computational complexity and relatively longer run-times. This study proposes an enhancement of some PCA-based methods (with relatively lower computational complexity and run-time) to overcome the challenges posed to the recognition module in the presence of multiple constraints. The study compared the performance of enhanced classical PCA-based method (HE-GC-DWT-PCA/SVD) to FaceNet algorithm (deep learning method) using expression variant face images artificially occluded at 30% and 40%. The study leveraged on two statistical imputation methods of MissForest and Multiple Imputation by Chained Equations (MICE) for occlusion recovery. From the numerical evaluation results, although the two models achieved the same recognition rate (85.19%) at 30% level of occlusion, the enhanced PCA-based algorithm (HE-GC-DWT-PCA/SVD) outperformed the FaceNet model at 40% occlusion rate, with a recognition rate of 83.33%. Although both Missforest and MICE performed creditably well as de-occlusion mechanisms at higher levels of occlusion, MissForest outperforms the MICE imputation mechanism. MissForest imputation mechanism and the proposed HE-GC-DWT-PCA/SVD algorithm are recommended for occlusion recovery and recognition of multiple constrained test images respectively.

In developing robots for warehouse logistics, image recognition and processing for moving targets are the cornerstone of subsequent work. In this paper, the Meanshift algorithm is extended to continuous image sequences, and the Camshift algorithm for motion target tracking in a warehouse environment is proposed to obtain effective tracking of targets through the probability distribution when the color of continuous images changes dynamically. Based on target tracking, a feature-matching-based image recognition method is constructed. The scene image is first treated with improved Gamma correction for light equalization, and then image features are extracted using SURF feature points. Regarding running time, the feature matching method is, on average, 2.03 seconds faster than FLDA and 0.96 seconds faster than PCAFLDA under the same external conditions. By optimizing the computational structure, the feature-matching method can address the need for efficiency in warehouse logistics.

Aiming at the problems of uneven light reflectivity on the spherical surface and high similarity between the stems/calyxes and scars that exist in the detection of surface defects in apples, this paper proposed a defect detection method based on image segmentation and stem/calyx recognition to realize the detection and recognition of surface defects in apples. Preliminary defect segmentation results were obtained by eliminating the interference of light reflection inhomogeneity through adaptive bilateral filtering-based single-scale Retinex (SSR) luminance correction and using adaptive gamma correction to enhance the Retinex reflective layer, and later segmenting the Retinex reflective layer by using a region-growing algorithm. The texture features of apple surface defects under different image processing methods were analyzed based on the gray level co-occurrence matrix, and a support vector machine was introduced for binary classification to differentiate between stems/calyxes and scars. Deploying the proposed defect detection method into the embedded device OpenMV4H7Plus, the accuracy of stem/calyx recognition reached 93.7%, and the accuracy of scar detection reached 94.2%. It has conclusively been shown that the proposed defect detection method can effectively detect apple surface defects in the presence of uneven light reflectivity and stem/calyx interference. [ABSTRACT FROM AUTHOR]

Poor quality images in Magnetic Resonance Imaging (MRI) may not provide enough information for visual interpretation of the affected areas of the human body. Cuckoo Search Constrained Gamma Masking for MRI Image Contrast Enhancement is a novel adaptive image enhancement technique described in this paper to improve image views and give computational support. Nature-inspired algorithms are widely applied in the arena of image enhancement for various optimization purposes. Cuckoo search is one of the prominent nature-inspired performance algorithms that we employed in this work for the enhancement of magnetic resonance imaging (MRI). We proposed a wavelet-based masking technique employing a cuckoo search algorithm whose masking value is corrected by gamma function for the contrast enhancement of MRI images. The cuckoo search algorithm can inevitably fine-tune the relation of nest building using genetic operatives like adaptive cusp and alteration. The proposed contrast enhancement scheme is examined quantitatively for different types of MRI images. Extensive simulation results compared with quantitative values have revealed that the traditional nest building of cuckoo search optimization is improved by adaptive gamma correction. Comparative analysis with the existing works establishes the usefulness of the proposed methodology over the other standard approaches. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Exploration of underwater resource play a vital role for nation development. Underwater surveillance systems play a crucial role in security applications, requiring accurate detection of suspicious objects in underwater images. However, the presence of noise, poor visibility, and uneven lighting conditions in underwater environments pose significant challenges for reliable object detection. This work proposes an integrated approach for underwater image de-noising, pre-processing, enhancement, and subsequent suspicious object detection by combining the DnCNN (Deep Convolutional Neural Network), CLAHE (Contrast Limited Adaptive Histogram Equalization), and additional image enhancement techniques. In addition to de-noising and pre-processing, it incorporate various image enhancement techniques to further improve object detection performance. These techniques include color correction, contrast adjustment, and edge enhancement, aiming to enhance the visual characteristics and saliency of suspicious objects in underwater images. To evaluate the effectiveness of proposed approach, this work conducted extensive experiments on an underwater image dataset containing diverse scenes and suspicious objects. The work compares proposed method with existing de-noising, preprocessing, and object detection techniques, analyzing the results using quantitative performance metrics, including precision, recall, and F1 score. The experimental results demonstrate that proposed integrated approach outperforms individual methods and achieves superior detection performance by enhancing the quality of underwater images and improving the visibility of suspicious objects. [ABSTRACT FROM AUTHOR]

Haze weather can lead to reduced visibility of captured images, affecting daily production and life. In this paper, a new defogging technique for multi-exposure images combined with prior algorithm is proposed. Firstly, the transmittance of different regions of the haze image is calculated to obtain more accurate prior information. Secondly, gamma correction is applied to the prior map to obtain a set of multiple underexposure images. Thirdly, for the difference between global features and local details in image defogging, the multiple underexposure image set is decomposed into base and detail layers using guided filtering, and the fusion weight maps of the base layers image patches and the detail layers Laplacian decomposition are constructed, respectively. Finally, the haze-free image is reconstructed and restored. The haze image is selected from a standard dataset with different haze concentrations and compared with the commonly used haze removal algorithms. The defogging effect of this algorithm has better performance in visual effect and objective evaluation index. [ABSTRACT FROM AUTHOR]

To address the issues of fuzzy scene details, reduced definition, and poor visibility in images captured under non-uniform lighting conditions, this paper presents an algorithm for effectively enhancing such images. Firstly, an adaptive color balance method is employed to address the color differences in low-light images, ensuring a more uniform color distribution and yielding a low-light image with improved color consistency. Subsequently, the image obtained is transformed from the RGB space to the HSV space, wherein the multi-scale Gaussian function is utilized in conjunction with the Retinex theory to accurately extract the lighting components and reflection components. To further enhance the image quality, the lighting components are categorized into high-light areas and low-light areas based on their pixel mean values. The low-light areas undergo improvement through an enhanced adaptive gamma correction algorithm, while the high-light areas are enhanced using the Weber–Fechner law for optimal results. Then, each block area of the image is weighted and fused, leading to its conversion back to the RGB space. And a multi-scale detail enhancement algorithm is utilized to further enhance image details. Through comprehensive experiments comparing various methods based on subjective visual perception and objective quality metrics, the algorithm proposed in this paper convincingly demonstrates its ability to effectively enhance the brightness of non-uniformly illuminated areas. Moreover, the algorithm successfully retains details in high-light regions while minimizing the impact of non-uniform illumination on the overall image quality. [ABSTRACT FROM AUTHOR]

There are different movements of AUV based on the design and if the AUV glides up and down underwater, so the images captured by the AUV cameras are depth variant images. In this scenario processing and getting high quality images and its information with the less battery power consumption has become a challenge. Recent AUV technology to capture the underwater images demands dedicated hardware unit to obtain clear underwater images without any haze. Since, underwater images are available in different color tones depending on the depth at which images are taken by the AUV cameras requires different processing methods. In this paper, a single hardware unit with Color tone determination prior (CTDP) algorithm is proposed to integrate with AUV's to process with different color tone images and produce good results. In our proposed image processing method, during the first phase of our work, we determined the color tone using CTDP and restored the red channel. In the second phase, white balancing and image fusion is performed to improve the underwater images for artifact free blending. Our method is experimented on various images of underwater image enhancement benchmark dataset (UIEBD). The results are compared with state-of-art underwater image enhancement methods for different metrics and it is observed that our method maintains the image quality in many benchmark images, it also shows improvement in non-reference metrics UIQM by 6% to 15%, by maintaining proper entropy and UCIQE and full reference metrics PSNR by 5% and SSIM by 11% as compared with previous works. Also, in our paper we proposed the power optimization techniques to be implemented on the proposed hardware unit. [ABSTRACT FROM AUTHOR]

Visual SLAM technology is widely used in underground search and rescue work, and the quality of image collected by robot directly determines the quality of image composition. At present, due to the influence of dust and light source con-ditions in underground coal mine, the enhancement effect of underground image needs to be improved. At present, the coal mine monitoring image enhancement effect needs to be improved due to the influence of dust and light source conditions in the coal mine.In order to solve this problem, this paper puts forward a HSV space combined with Adaptive Gamma Correcti-on with Weighting Distribution (AGCWD) homomorphic filtering method.Firstly, to solve the problem of over-enhancement of the highlight and shadow areas existing in the classical homomorphic filtering algorithm, the AGCWD algorithm is used to carry out adaptive gamma correction for the probability density of the V component in HSV space, and the new probability distribution is non-linearly mapped to improve the applicability of the homomorphic filtering to the high light and shadow ar-eas.Then single-parameter homomorphic filter is used for processing to alleviate the problem of difficult parameter selection c-aused by multiple parameters.In order to preserve the detail of the image, and then the results of single parameter after the homomorphic filtering to carry on the Contrast Limited Histograme Equalization(CLAHE);Finally, HSV inverse transformation is carried out to obtain the image in RGB space, and image enhancement is completed.By the improved homomorphic filterin-g algorithm, CLAHE algorithm and classical homomorphic filtering algorithm proposed in this experiment, the result image mean, standard deviation, peak signal-to-noise ratio (PSNR), information entropy and other indicators are evaluated.Compared with the CLAHE algorithm, the improved homomorphic filtering algorithm is improved by 65.29%, 21.58%, 17.03% and 5.18% respectively, and compared with the classical homomorphic filtering algorithm, it is improved by 52.07%, 40.73%, 36.23% and 8.96% respectively.The experimental data show that the improved homomorphic filtering algorithm can enhance the b-rightness and contrast of the image and keep the detail information of the image. At the same time, the overenhancement p-henomenon of classical homomorphic filtering on the image with large gap between light and dark is suppressed to a certainn extent.

Images captured in low illumination environments can affect people’s judgment of image information. To raise the visual effect of low illumination images and preserve the integrity of image information, this study proposes two algorithms. One is an image enhancement algorithm for low illumination, which combines the WASOBI image denoising method for deep denoising of images and adaptive gamma correction for brightness adjustment of images. The other is an image brightness equalization algorithm for uneven lighting. This algorithm divides the image according to the illumination area, and uses guided filtering and adaptive gamma correction to equalize the brightness of the image. The laboratory outcomes demonstrate that the proposed image enhancement algorithm can effectively reduce the noise level of the image and adjust the brightness of the image, and the algorithm has a certain degree of stability. The subjective evaluation score of the image processed using the brightness equalization algorithm is 4.7, indicating that the image has good visual effects. The objective evaluation results prove the capability of the equilibrium algorithm, and comparing to other algorithms, the algorithm has the shortest operation time, only 0.9452 seconds. Therefore, the enhancement algorithm and brightness equalization algorithm proposed in the article have certain application value.

Clouds become an important part of human life and are studied in several disciplines in the form of important analyses in some applications. Examples of application of cloud analysis on solar panels or photovoltaics, accurate weather forecasts, accuracy of rainfall predictions, application in the field of meteorology, imaging of the sky in some cases, air humidity survey, and the case of turbulence on Aircraft caused by clouds cumulonimbus. The structure and shape of the clouds are continuously changing, becoming an interesting part to detect. The cloud detection process can be done by taking several samples of imagery from the cloud and the image processing process is done. Most research processes RGB cloud imagery into HSV cloud imagery, Some research using the image detection method of flying apply the channel's convolution R-B, R/B, B-RB+R, and chroma C = max(R, G, B)-min(R, G, B. Gamma correction has an efficient characteristic of storing and dividing imagery by small bits, thus the study proposed an image detection development using automatic gamma correction, with ground truth being Image data from SWIMSEG Nanyang Technological University Singapore. The proposed method in the proposed study obtained a precision value and better computing time with a precision value of 0.93 and a computational time of 0.71 sec.

With the development of the robot in electricity, more and more autonomous live-line maintenance robots (ALMRs) have been developed and put into use. However, in outdoor environment, complicated and uneven illumination lead to huge challenges for visual feedback and target recognition of ALMRs. Aiming at easing the disturbance brought by the strong uneven illumination, we collect a Hot-Line dataset containing fieldwork photos of the ALMR and propose an image enhancement method for uneven illumination images based on image brightness segmentation and multi-methods fusion. Through image segmentation based on illuminance, the proposed algorithm enhances the over- and under-illuminated parts of the image differently while taking the approximate illumination component as a reference. We introduce an adaptive weighted summation strategy to ease the problem of edge transition in the output. The proposed algorithm improves the overall performance of a fieldwork image of ALMR properly, making the image clearer and better. For six indexes (Laplacian, SMD2, Energy of Gradient (EOG), and Entropy for image clarity; Structural similarity index measure (SSIM) and Peak signal-to-noise ratio (PSNR) for the degree of image information retention), the proposed method provided good results on both our Hot-Line dataset (for example, on EOG, the proposed method achieves nearly double the performance index value than CLAHE) and other image datasets, and finished the enhancement within a relatively short time (within 0.02s with image size 275 × 275). The proposed algorithm has been verified on an ALMR for the power distribution network and archived good results. [ABSTRACT FROM AUTHOR]

Image dehazing is a severe and challenging problem due to its ill-posed behavior and is highly desired in various vision based applications such as computer vision, image processing, computational photography, remote sensing, outdoor driving assistance, and video surveillance, etc. Therefore, we proposed a novel effective edge-aware weighting filter-based structural patch decomposition multi-exposure image fusion method for single image dehazing. It removes haze firmly and preserves edge information precisely in the flat and sharp regions. The proposed method comprises four steps. First, a set of four gamma coefficients ( γ = 2 , 4 , 6 , 8 ) is applied to the input hazy image and obtained the corresponding underexposed outcomes, respectively. Then, the structural patch decomposition method decomposes each underexposed image into three independent elements: signal strength, signal structure and signal mean intensity. Next, a novel, effective edge-aware weighting-based guided image filter is used to refine each decomposed image patch. Finally, these refined images are fused to achieve a compelling haze-free image. The proposed method removes halo artifacts, over smoothing and color cast strongly, and preserves edge information precisely in both flat and sharp regions. The theoretical analysis and tested outcomes prove that the proposed haze removal method can produce faster and more effective outcomes than the existing haze removal methods. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of change detection, a novel synthetic aperture radar (SAR) image change detection method based on Gamma correction and fuzzy local information c-means clustering (FLICM) model is proposed in this paper. Firstly, the original SAR images are filtered by speckle reducing anisotropic diffusion filter; secondly, the difference image (DI) is obtained by log-ratio operator; thirdly, the DI is processed by the Gamma correction operation; finally, the FLICM model is used to get the change detection result. Experimental results on four groups of SAR images demonstrate that the proposed algorithm has a good performance than many competitive approaches in terms of SAR image change detection. [ABSTRACT FROM AUTHOR]

Underwater image enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Captured underwater images usually suffer from contrast degradation, low illumination, color cast, and noise. Many underwater image enhancement and restoration algorithms have been developed but are not able to solve all these problems. In this paper, a new single image retinex algorithm using gamma correction is proposed. Here the input image is decomposed into illumination and Reflectance. Illumination contains brightness variation, and Reflectance preserves the details information. Then Reflectance decomposed into multiple layers, which carried out gamma correction and contrast enhancement. Whereas illumination carried out brightness adjustment. Finally, these layers are combined to obtain an enhanced image. The proposed method produces high-quality enhanced images compared to the existing state-of-art method such as the Hue-preserving-based approach for underwater color image enhancement, Underwater image processing using a hybrid technique, and Underwater dark channel before using a guided image filter. The proposed method is tested for the underwater image enhancement benchmark data set and compared with the existing state-of-art method. Qualitative and quantitative results demonstrate the effectiveness of the proposed method in terms of seven parameters such as measure of enhancement (EME), discrete entropy (DE), peak signal to noise ratio (PSNR), Structure similarity index measure (SSIM), underwater color image quality evaluation (UCIQE), underwater image quality measure (UIQM), and patch-based contrast quality index (PCQI) for underwater images. Six parameters of the proposed method performed better compared to an existing method. The visual appearance of the output image of the proposed method has a very high quality. [ABSTRACT FROM AUTHOR]