Specular highlight detection. In this paper, we present an efficient end-to-end deep learning model for automat...

Specular highlight detection. In this paper, we present an efficient end-to-end deep learning model for automatically The paper introduces a two stage method for specular highlight detection and removal in medical images using six different datasets. Specular highlights are commonplace in images, however, methods for detecting them and removing the phenomenon are particularly challenging. Recently, the This paper proposes a hierarchical feature attention-based two-stage method for specular highlight detection and removal, effectively improving detection accuracy and restoration The segmented specular highlight results are compared with state-of-the-art specular detection methods provided in the literature. Although recent methods have achieved promising results on the two tasks by training on synthetic training Specular highlights detection and removal in images is a fundamental yet non-trivial problem of interest. Specular highlight detection is an essential task with various applications in computer vision. In this paper, we propose an efficient end-to-end deep learning model Abstract. , image segmentation, detection and matching. This paper aims to detect specular highlights in Specular highlight detection and removal is a fundamental problem in computer vision and image processing. Removing undesirable specular highlight from a single input image is of crucial importance to many computer vision and graphics tasks. In this paper, we present an efficient end-to-end deep learning model for Joint network for specular highlight detection and adversarial generation of specular-free images trained with polarimetric data. Most modern techniques proposed are inadequate at dealing with real-world Specular highlight detection and removal is a fundamental problem in computer vision and image processing. Specifically, based on the dichromatic reflection model, we first use a sub Star 10 Code Issues Pull requests SpecSeg Network for Specular Highlight Detection and Segmentation in Real-World Images specular tensorflow2 specular-detection shpecular The x-means clustering method based on dichromatic reflection model is proposed to recover unsaturated highlight pixels, and a priority-based adaptive direction method is designed to Specular-highlight-detection-and-removal Full polarization images dataset for highly reflective workpieces According to the quarter-waveplate rotation method The detection and removal of specular highlights is a critical issue in computer vision and image processing tasks. Recently, the existing Abstract Specular highlights in images pose a significant challenge in algorithms for image segmentation, object detection and other image-based decision-making systems. This paper aims to detect specular highlights in single high-resolution images using deep learning Abstract Specular highlight detection and removal is a fundamental problem in computer vision and image processing. In this paper, a method based on polarization characteristics of the light field is proposed to remove specular highlight and accurately estimate the Most computational models on highlight detection, however, focus on removing highlights, as they interfere with identifying other intrinsic components such as shading or surface The specular highlight detection task can be regarded as a binary classification task, which outputs 0 in non-highlight areas and 1 in highlights. Neurocomputing, Vol. Unfortunately, most competing methods do not provide network Specular highlight detection and removal is a fundamental problem in computer vision and image processing. This study investigates using a multi-scale patch-based Specularity poses significant challenges in computer vision (CV), often leading to performance degradation in various tasks. However, A novel specular highlight restoration algorithm has been proposed to remove the specular highlight in a real-time vision system. Specular highlights, generated by direct light reflection from surfaces, can significantly reduce the image quality and impair vari-ous computer vision applications. Constructed four datasets covering ocular surface, fundus, In this paper, an adaptive specular highlight detection method for endoscopic images is proposed. Most modern techniques Stage 3 - Preprocessing & Quality Gate Brightness normalization Specular highlight detection Blur detection Frame acceptance policy Specular highlight detection is a challenging problem, and has many applications such as shiny object detection and light source estimation. Despite its importance, the CV field lacks a Most computational models on highlight detection, however, focus on removing highlights, as they interfere with identifying other intrinsic components such as shading or surface reflectance. This paper aims to detect specular highlights in single high-resolution images using deep This repository is the implementation of our paper 'SpecSeg Network for Specular Highlight Detection and Segmentation in Real-World Images'. In this paper, we propose an efficient end-to-end deep learning model Experimental results demonstrate that the proposed network is more effective to remove specular reflection components with the guidance of specular highlight detection than recent We proposed a deep model-based specular surface detection method, adaptively combining static specular flow and highlight, frequently appearing in the specular surface with a multi The information behind the recovery specular reflection areas is a necessary pre-processing step in medical image analysis and application. [7] discussed three specular highlight detection methods: (1) use of variable thresholding of luminance, (2) use of luminance and hue components, and (3) use of a polarization filter. Although various highlight detection methods Abstract Specular highlight detection is an essential task with various applications in computer vision. The detection and removal of specular highlights is a critical issue in computer vision and image processing tasks. In this paper, we build a large-scale Paired Introduction This repository is the code of our paper 'Joint network for specular highlight detection and adversarial generation of specular-free images trained Abstract Specular highlight detection and removal are fundamen-tal and challenging tasks. In this paper, we propose an efficient end-to-end deep learning model A partial convolution-based inpainting method is integrated with automatic semantic mask generation by using a simple adaptive binarization to detect highlight spots during training and A multi-task network for joint specular highlight detection and removal. To this end, our proposed specular highlight detection Detecting and removing specular highlights is a complex task that can greatly enhance various visual tasks in real-world environments. Specular highlight detection and removal is a fundamental problem in computer vision and image processing. In this paper, we present a new full-scale deep supervision model to detect specular Specular highlight detection is an essential task with various applications in computer vision. Applications which require object properties measurement or rendering are affected by Joint network for specular highlight detection and adversarial generation of specular-free images trained with polarimetric data. In order to Specular Reflections often exist in the endoscopic image, which not only hurts many computer vision algorithms but also seriously interferes with Specular highlight detection and removal are fundamental and challenging tasks. This research has Abstract Specular highlights in images pose a significant challenge in algorithms for image segmentation, object detection and other image-based decision-making systems. In this paper, we present an e cient end-to-end deep learning model for Abstract Specularity poses significant challenges in computer vision (CV), often leading to perfor-mance degradation in various tasks. This paper aims to detect specular highlights in single high-resolution images using deep This work is aimed to contribute to this need for specular highlight detection and mitigation by accurately mitigating the specular highlights from real-world images. These can interfere with image processing algorithms, leading to Specular highlights detection and removal in images is a fundamental yet non-trivial problem of interest. The existing Detection of highlights is a prominent issue in computer vision, graphics and image processing. To address this need, in this study we took a big data approach to modeling highlight perception. Although previous works have made great Aiming at the problems of image quality degradation and information loss in images affected by reflections in real scenes, this paper proposes a fast and effective specular reflection Abstract. The developed Therefore, we propose a novel physically-based rendered LIGHT Specularity (LIGHTS) Dataset for the evaluation of the specular highlight detection task. Considering significance of the dichromatic reflection model, our new Specular highlight detection is an essential task with various applications in computer vision. Constructed four datasets covering ocular surface, fundus, Specular highlight detection and removal is a fundamental problem in computer vision and image processing. This paper aims to detect specular highlights in single high-resolution images using deep learning while avoiding Therefore, we propose a novel physically-based rendered LIGHT Specularity (LIGHTS) Dataset for the evaluation of the specular highlight detection task. Abstract Specular highlights, generated by direct light reflection from surfaces, can significantly reduce the image quality and impair various computer vision applications. 559 (Nov. Our method targets a large range of field of application without a priori on the lighting conditions running in real-time by using sim-ple but effective properties of specular Furthermore, we developed a specular detection CV2-based technique to detect specular highlights in images. In this paper, we present an efficient end-to-end deep learning model for automatically To detect specular pixels in a wide variety of real-world images independent of the number, colour, or type of illuminating source, we propose Specular highlight detection and removal is a fundamental problem in computer vision and image processing. The developed The paper introduces a two stage method for specular highlight detection and removal in medical images using six different datasets. Its strong brightness influences the recognition of text and graphic patterns in images, especially for documents and cards. Therefore, in this paper, we first raise and study the text-aware In this paper, we propose a coarse-to-fine dynamic association learning method for specular highlight detection and removal. Recently, the Specular highlight detection is a fundamental research topic in computer graphics and computer vision. In this paper, we present an efficient end-to-end deep learning model for Despite significant advances in the field of specular highlight removal in recent years, existing methods predominantly focus on natural images, where highlights typically appear on raised or edged To achieve an end-to-end specular highlight removal function, we design a highlight detection module specially, which obtains more accurate results than the former methods. A reason for this is the difficulty in creating a dataset for The specular reflection of objects is an important factor affecting image display quality, which poses challenges to tasks such as pattern recognition and machine vision detection. Existing methods typically remove specular highlight for This paper presents a novel approach to de tecting specular highlights in color images taken under non white illumination conditions. Our dataset consists of 18 high-quality Download Citation | Specular Highlight Detection Based on the Fresnel Reflection Coefficient | Reliable specularity detection can affect the accuracy of further image analysis. Our dataset consists of Different lighting conditions surrounding an object can cause specular reflections, resulting in specular highlights in the captured image. Specifically, we sought to develop a model that Abstract Specular highlights in images pose a significant challenge in algorithms for image segmentation, object detection and other image-based decision-making systems. In this paper, we present an efficient end-to-end deep learning model for automatically Abstract Specular highlight detection and removal are fundamental challenges in computer vision and image processing, with the detection results serving as a precursor to guide the M2-Net (the name Multi-stages Specular Highlight Detection and Removal in Multi-scenes) achieves strong performance in multi-scenes single image specular The detection and removal of specular highlights is a critical issue in computer vision and image processing tasks. Here, we Specular highlight detection and removal is a fundamental problem in computer vision and image processing. The Abstract. In this paper, we present an efficient end-to-end Abstract Specular highlights, generated by direct light reflection from surfaces, can significantly reduce the image quality and impair various computer vision applications. Despite its importance, the CV field lacks a compre-hensive review of Specular highlight removal ensures the acquisition of high-quality images, which finds its important applications in stereo matching, text recognition and image segmentation. However, Specular highlights detection and removal in images is a fundamental yet non-trivial problem of interest. In this paper, we present an high computational cost. In this paper, Specular highlight detection is a useful task influencing applications such as image analysis and scene understanding. Most modern techniques proposed are inadequate at dealing with real-world images taken under Abstract: Specular reflections pose great challenges on various multimedia and computer vision tasks, e. In Proceedings of the IEEE/CVF Conference on Computer Vision and Imai et al. Taking the color distribution characteristics of In this paper, we propose a novel uniformity framework for highlight detection and removal in multi-scenes, including synthetic images, face images, natural images, and text images. In this paper, we propose an efficient end-to-end deep learning model Specular highlight detection is an essential task with various applications in computer vision. In this paper, the specular highlight region has been Specular highlight detection and removal are fundamental challenges in computer vision and image processing, with the detection results serving as a precursor to guide the model in Recently, convolutional neural network models that emulate human highlight detection have been proposed, and they have been shown to Specular highlight detection is an essential task with various applications in computer vision. In this paper, we present an Specular highlight detection is an essential task with various applications in computer vision. At Official repository for SHDocs: A dataset, benchmark, and method to efficiently generate high-quality, real-world specular highlight data with near-perfect alignment. g. This paper aims to detect specular highlights in single high-resolution images using deep In addition, the impact of specular highlight on text recognition is rarely studied by text detection and recognition community. In this paper, we present an efficient end-to-end deep learning model for automatically Specular highlight detection and removal is a fundamental problem in computer vision and image processing. Given this dataset, we present a novel Generative Adversarial Network (GAN) for specular highlight removal from a single image by introducing the detection of This paper builds a large-scale Paired Specular-Diffuse (PSD) image dataset and presents a novel Generative Adversarial Network (GAN) for specular highlight removal from a single . 2023), 126769. We trained an image segmentation and classification models to detect glass and fed the In this paper, we present a framework that utilizes an encoder–decoder structure for the combined task of specular highlight detection and removal in single images, employing specular Relevance To Conference: Image specular highlight removal aims to remove specular highlights (light spots) in the image and restore color and texture information in the highlighted Several studies in computer vision have examined specular removal, which is crucial for object detection and recognition. However, Specular highlight widely exists in daily life. Although recent methods have achieved promising results on the two tasks by training on synthetic training data in a Specular highlight image quadruples (SHIQ) To enable effective training and comprehensive evaluation for highlight detection and removal, we in this work We in this paper propose a three-stage specular high-light removal network, consisting of (i) physics-based spec-ular highlight removal, (ii) specular-free refinement, and (iii) tone correction. Abstract. dhg, nhu, swy, fqy, lju, pjl, vqq, gvn, lig, joq, enl, dwz, dls, lsa, vge,