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Using color histograms in automatic emotion recognition systems faces different issues. One of the important challenges is to determine the appropriate number of bins in the color histogram to achieve the highest recognition performance possible with minimal computations. This research focuses on emotion recognition induced by visual contents of images, or REVC for short, using ARTphoto dataset. Twenty-two different classifiers are used with color histograms in both RGB (red, green, blue) and HSV (hue, saturation, value) color spaces across different numbers of bins, and overall performance of each bin size is compared with that of other bin sizes. The research findings show that the performance of REVC system does not improve in terms of overall sensitivity rate, when the number of bins in color histogram is increased. Moreover, this paper identifies the advantage of using HSV color space over RGB in using color histogram for REVC systems. Furthermore, findings recognize the optimum number of bins in both RGB and HSV color spaces, and ANOVA (analysis of variance) is used to analyze experimental data, which identifies the optimum color histogram bin size used for HSV color space is significantly better than that used for RGB color space in REVC systems.

Poor visibility not only affects the consumer photography but also computer vision application. Low-light image enhancement explains the image details or perception of information in images for human viewers and understanding of imagery. Using the pixel values existing in the digital image, the details of low-light images are boosted under low illumination. The primary purpose of enhancing low-vision image is to increase visibility of the image. Dealing with noise is a significant challenge while enhancing. Previous studies have offered a variety of approaches for enhancing low-vision images. Low contrast, low brightness, severe noise, dark colors are the issues with image enhancing in low-light situations. Monochromatic atmospheric scattering model is used to form proposed absorption light scattering model. The concealed details and hidden features were reproduced with sufficient and homogeneous illumination. This strategy will help to achieve high result in terms of structure, details and local feature similarity. Here, some mathematical formulas were used to produce brightest image, which is produced from monochromatic ASM and absorption light scattering model. When we compare absorption light scattering method with other techniques, it will produce better result. This strategy highlights major details of absorption light scattering model. This method will produce better result in terms of contrast, brightness, sharpness, etc.

This paper proposes an approach to improve color images suffering from low dynamic range, by employing both histogram matching and histogram stretching techniques. Firstly, the color image is transformed from RGB into HSV color space, in which the color information is separated from intensity. Then, an appropriate reference image is selected by comparing component V of the enhancing image with component V of the data base images using a similarity measure. This selected image is used as the goal image in histogram matching algorithm in order to enhance image brightness (Automatic Histogram Matching). Secondly, components V and S are linearly stretched in order to recover the image color information. Finally, using the treated V and S components and untreated component of H, the enhanced image is obtained in RGB color space by inverse transform. The qualitative and quantitative results show that the contrast and color of resultant images are greatly improved using the proposed method, which outperforms the current state-of-the-art methods.

Cast shadow affects computer vision tasks such as image segmentation, object detection and tracking since objects and shadows share the same visual motion characteristics. This unavoidable problem decreases video surveillance system performance. The basic idea of this paper is to exploit the evidence that shadows darken the surface which they are cast upon. For this reason, we propose a simple and accurate method for detection of moving cast shadows based on chromatic properties in RGB, HSV and YUV color spaces. The method requires no a priori assumptions regarding the scene or lighting source. Starting from a normalization step, we apply canny filter to detect the boundary between self-shadow and cast shadow. This treatment is devoted only for the first sequence. Then, we separate between background and moving objects using an improved version of Gaussian mixture model. In order to remove these unwanted shadows completely, we use three change estimators calculated according to the intensity ratio in HSV color space, chromaticity properties in RGB color space, and brightness ratio in YUV color space. Only pixels that satisfy threshold of the three estimators are labeled as shadow and will be removed. Experiments carried out on various video databases prove that the proposed system is robust and efficient and can precisely remove shadows for a wide class of environment and without any assumptions. Experimental results also show that our approach outperforms existing methods and can run in real-time systems.

This paper presents a low-light image enhancement method using a convolutional neural network (CNN). Given a lowlight input image, the proposed method converts RGB color space to CIELAB color space. The luminance and chrominance components are separately enhanced. The luminance channel is enhanced using a CNN to enhance the brightness. On the other hand, the chrominance channels are enhanced using a dilated CNN to reduce the color distortion. Experimental results demonstrate that the proposed method can successfully enhance low-light images of a vehicular imaging system without color distortion.

In order to automate agriculture, development in the robotics equipped with machine vision sensors applied to Precision agriculture is a demanding solution for various applications in the agriculture. Machine vision system necessary for crop row and weed detection. This paper proposes an automatic furrow detection system based on identifying crop rows in maize fields in the presence of weeds. The image quality is affected by different lighting conditions and gaps between the crop furrows due to lack of germination. The proposed image processing method consists of four different processes. First, image segmentation based on HSV (Hue, Saturation, Value) decision tree. The HSV color space used to discriminate crops, weeds, sky, and soil. The region of interest (ROI) is defined by filtering each of the HSV channels between the maximum and minimum threshold values. Then, the noises in the images are eliminated by selecting pixel values. Further, mathematical morphological processes, that is, erosion to remove smaller objects followed by dilation to enlarge the boundaries of regions of foreground pixels are applied. To detect the position of crop rows, ROI was defined by creating a binary mask. The Hough Transform and blob analysis were applied to detect crop furrows. The experimental results show that the method is effective.

Self-supervised network for low-light traffic image enhancement based on deep noise and artifacts removal

Enhancing low-light images with color distortion and uneven multi-light source distribution presents challenges. Most advanced methods for low-light image enhancement are based on the Retinex model using deep learning. Retine... | Find, read and cite all the research you need on Tech Science Press

Recently, compressive sensing (CS) schemes have been studied as a new compression modality that exploits the sensing matrix in the measurement scheme and the reconstruction scheme to recover the compressed signal. In addition, CS is exploited in medical imaging (MI) to support efficient sampling, compression, transmission, and storage of a large amount of MI. Although CS of MI has been extensively investigated, the effect of color space in CS of MI has not yet been studied in the literature. To fulfill these requirements, this article proposes a novel CS of MI based on hue-saturation value (HSV), using spread spectrum Fourier sampling (SSFS) and sparsity averaging with reweighted analysis (SARA). An HSV loop that performs SSFS is proposed to obtain a compressed signal. Next, HSV–SARA is proposed to reconstruct MI from the compressed signal. A set of color MIs is investigated, such as colonoscopy, magnetic resonance imaging of the brain and eye, and wireless capsule endoscopy images. Experiments were performed to show the superiority of HSV–SARA over benchmark methods in terms of signal-to-noise ratio (SNR), structural similarity (SSIM) index, and measurement rate (MR). The experiments showed that a color MI, with a resolution of pixels, could be compressed by the proposed CS at MR of 0.1, and could be improved in terms of SNR being 15.17% and SSIM being 2.53%. The proposed HSV–SARA can be a solution for color medical image compression and sampling to improve the image acquisition of medical devices.

In this paper, we propose a novel hue-correction scheme based on the constant-hue plane in the RGB color space for deep-learning-based color-image enhancement. Existing hue-preserving image-enhancement methods cannot be applied to state-of-the-art enhancement methods such as deep-learning-based ones. Our main contributions are a discussion on the enhancement performance and the hue distortion of existing image-enhancement methods as well as the first attempt to make hue correction applicable to any existing image-enhancement methods including deep-learning-based ones. This novel scheme is carried out on the basis of the constant-hue plane in the RGB color space. In simulations, we first evaluated conventional image-enhancement methods in terms of the enhancement performance and hue distortion by using five objective metrics: the maximally saturated color similarity, the hue difference in CIEDE2000, discrete entropy, HIGRADE, and NIQMC. The experimental results show that recent deep-learning-based methods have a higher enhancement performance but cause images to be hue-distorted. In addition, the proposed scheme is demonstrated to be effective for suppressing hue distortion even under the use of deep-learning-based enhancement methods. Furthermore, it allows us not only to correct hue but also to maintain the performance of image-enhancement methods.

Nowadays understanding of image processing and pattern recognition is the great focus to develop an application of image segmentation. Edge detection is the fundamental technique that can provide information of the boundaries in different objects in an image. Selecting a suitable edge detection algorithm is important to obtain the best performance of segmentation. This paper presents an analysis of implementation for various edge detection techniques on color spaces. We implement Sobel, Prewitt, Robert, and Laplacian edge detection on Hue, Saturation, Value (HSV) and YUV color space by using threshold = 65. We calculated the mean square error (MSE), root mean square error (RMSE), peak signal to noise ratio (PSNR) and execution time as a comparison. This paper tested images from Berkeley Segmentation Dataset (BSDS). The results showed that Robert edge detection in HSV color space especially hue channel can be utilized to obtain more precise boundaries in different objects and it is also quick to compute.

Skin cancer is the most common type of cancer, a life-threatening condition that leads to serious health problems if not detected early, and its incidence is increasing worldwide. In recent years, computer vision and decision support systems have been used for disease detection in dermoscopic images. Furthermore, it has been observed that data representation methods affect the detection performance of these models, and the effect of color information on transformer-based models has not been sufficiently investigated. This study used the International Skin Imaging Collaboration (ISIC) 2017 dataset consisting of RGB images, and these images were converted into the HSV, LAB, and YCbCr color spaces. Transformer-based models, including visual transformer (ViT), swin transformer, data efficient image transformer (DeiT), and label-free self-distillation (DINO), were used for benign and malignant classification. According to the classification performance results, RGB and HSV color spaces particularly in the DeiT and Swin models, stable and high accuracy values ​​were obtained. It was observed that the ViT and DINO models were more sensitive to color space transformations and achieved lower classification performance compared to other models. The highest performance in skin cancer classification was achieved with the DeiT model trained in the RGB color space, with the highest accuracy (0.7668). Furthermore, the explainability-based gradient-weighted class activation mapping (Grad-CAM) method was used to analyze where the models focused in image regions when making classification decisions. This study shows the effect and usability of color space transformations in transformer-based models for skin cancer classification and offers a comparative contribution to the literature.

Low-light image enhancement aims to enhance the visibility and contrast of low-light images while eliminating complex degradation issues such as noise, artifacts, and color distortions. Most existing low-light image enhancement methods either focus on quality while neglecting computational efficiency or have limited learning and generalization capabilities. To address these issues, we propose a Bilateral Enhancement Network with signal-to-noise ratio fusion, called BiEnNet, for lightweight and generalizable low-light image enhancement. Specifically, we design a lightweight Bilateral enhancement module with SNR (Signal-to-Noise Ratio) Fusion (BSF), which serves the SNR map of the input low-light image as the interpolation weights to dynamically fuse global brightness features and local detail features extracted from a bilateral network and achieve differentiated enhancement across different regions. To improve the network’s generalization ability, we propose a Luminance Normalization (LNM) module for preprocessing and a Dual-Exposure Processing (DEP) module for post-processing. LNM divides the channels of input features into luminance-related channels and luminance independent channels, and reduces the inconsistency of the degradation distribution of input low-light images by only normalizing the luminance-related channels. DEP learns overexposure and underexposure corrections simultaneously by employing the ReLU activation function, inverting operation, and residual network, which can improve the robustness of enhancement effects under different exposure conditions while reducing network parameters. Experiments on the LOL-V1 dataset shows BiEnNet significantly increased PSNR by 8.6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} and SSIM by 3.6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} compared to FLW-Net, reduced parameters by 98.78\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}, and improved computational speed by 52.64\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} compared to the classical KIND.

Malaria is a disease in human caused by Plasmodium parasite. Misdiagnosis and improperness in medical treatment towards this disease can lead to the death of patients. A number of digital image analysis-based research works to minimize the misdiagnosis has been developed. This research aims to segment the form of parasitic cells of Plasmodium using the combination of the red (R) channel in the RGB color space and saturation (S) channel in the HSV color space. The images used were parasitic cells of Plasmodium vivax, consisting of the phases of trophozoite, schizonts, and gametocytes. Each of which comprises ten samples. The process was begun by improving the image quality continued by separating the red channel in the RGB color space and by converting the image of RGB into the HSV image to obtain the saturation (S) channel. These two color channels were then combined in the form of a grayscale image. The further process was conducted by changing those grayscale image into the binary image using the Otsu method and the morphological operation to form it as the masking image. Of 30 tested images, the segmentation of the red (R) channel and saturation (S) channel combination achieved the accuracy of 93.33%, while the segmentation using saturation (S) channel produces result in the accuracy of 66.67%.

This study introduces an innovative unsupervised low-light image enhancement method, termed GDLLE. The key innovation lies in decoupling the luminance and content representations of an image, thereby establishing a bidirectional mapping between low-light and normal-light images, which significantly enhances the visual quality of low-light images. In contrast to traditional supervised learning approaches that rely on paired datasets, the GDLLE method does not require paired low-light and normal-light image pairs. Instead, it enhances low-light images by extracting luminance masks and content information from unpaired images. Furthermore, the study incorporates an adaptive normalization strategy and a multi-scale enhancement framework to optimize both the global structure and local details of the image. To further improve the enhancement performance, a self-supervised mechanism based on cyclic consistency is introduced, which leverages cyclic consistency loss and self-feature preservation loss to ensure the quality of the generated images. Experimental results demonstrate that the GDLLE method outperforms existing approaches on several benchmark datasets, compared with the baseline models, GDLLE has achieved an improvement of 0.46 dB in the PSNR metric, validating its effectiveness and superiority in the unsupervised low-light image enhancement task. This research offers a novel solution in the field of low-light image enhancement, with promising potential for superior image quality improvement in practical applications.