A Global Optimization Method for Specular Highlight Removal From a Single Image

Xia, Wenyao; Chen, Elvis C. S.; Pautler, Stephen E.; Peters, Terry M.

doi:10.1109/access.2019.2939229

Cited by 25 publications

(17 citation statements)

References 40 publications

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“…Son et al [15] proposed a convex optimization framework to effectively remove the specular highlight from chromatic and achromatic regions of natural images. Xia et al [16] proposed a global optimization method for specular highlight removal. The method consists of estimating diffuse chromaticity by correcting hue and saturation on highlighted regions and estimating diffuse and specular reflection coefficients using convex optimization with double regularization.…”

Section: A Dichromatic Reflection Model-based Approachesmentioning

confidence: 99%

An Effective Algorithm for Specular Reflection Image Enhancement

et al. 2021

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Specular highlights often exist in real-world images due to the material property of objects and the capturing environments. High light in real-life scenes can reduce image quality and cause loss of image information, to overcome this, we propose an effective specular reflection image enhancement algorithm. First, the scene depth map of specular reflection image was obtained by the color attenuation prior method; then, two adaptive adjustment factors were developed to optimize the transmission map for specular reflection images and L 0 gradient minimization filter was used to eliminate halo artifacts. Finally, the nonuniform illumination compensation strategy based on the YUV color space was used to expand the dynamic range of the image and increase the saturation of the image. Experimental results via subjective and objective evaluations demonstrate that the proposed method can provide better visual quality than other methods and can effectively enhance the information in the specular reflection image.

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Section: A Dichromatic Reflection Model-based Approachesmentioning

confidence: 99%

An Effective Algorithm for Specular Reflection Image Enhancement

et al. 2021

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“…The spectral energy distribution of the reflected light from an object creates a specular effect when a video is captured [24,25]. Specularity causes negative impacts on the computer vision task, such as classification, segmentation, object detection [25], etc.…”

Section: Pre-processingmentioning

confidence: 99%

Two Stage Classification with CNN for Colorectal Cancer Detection

Sharma¹,

Bora²,

Kasugai³

et al. 2020

Oncologie

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In this paper, we address a current problem in medical image processing, the detection of colorectal cancer from colonoscopy videos. According to worldwide cancer statistics, colorectal cancer is one of the most common cancers. The process of screening and the removal of pre-cancerous cells from the large intestine is a crucial task to date. The traditional manual process is dependent on the expertise of the medical practitioner. In this paper, a two-stage classification is proposed to detect colorectal cancer. In the first stage, frames of colonoscopy video are extracted and are rated as significant if it contains a polyp, and these results are then aggregated in a second stage to come to an overall decision concerning the final classification of that frame to be neoplastic and non-neoplastic. In doing so, a comparative study is being made by considering the applicability of deep learning to perform this two-stage classification. The CNN models namely VGG16, VGG19, Inception V3, Xception, GoogLeNet, ResNet50, ResNet100, DenseNet, NASNetMobile, MobilenetV2, InceptionResNetV2 and fine-tuned version of each model is evaluated. It is observed that the VGG19 model is the best deep learning method for colonoscopy image diagnosis.

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“…It needs to set reasonable empirical parameters, which may be effective for small areas of highlights, but often only half of the restoration can be completed for larger areas of highlights. Two-color reflection model-based methods [ 23 , 24 ] utilize intensity ratios to separate the specular component and diffuse reflection in an image. It has a certain effect on the highlight removal of natural images, but the restoration performance of endoscopic images is relatively poor, and the restored images will produce problems such as discordant color distortion.…”

Section: Introductionmentioning

confidence: 99%

Specular Reflections Detection and Removal for Endoscopic Images Based on Brightness Classification

Nie

Chen

et al. 2023

Sensors

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Specular Reflections often exist in the endoscopic image, which not only hurts many computer vision algorithms but also seriously interferes with the observation and judgment of the surgeon. The information behind the recovery specular reflection areas is a necessary pre-processing step in medical image analysis and application. The existing highlight detection method is usually only suitable for medium-brightness images. The existing highlight removal method is only applicable to images without large specular regions, when dealing with high-resolution medical images with complex texture information, not only does it have a poor recovery effect, but the algorithm operation efficiency is also low. To overcome these limitations, this paper proposes a specular reflection detection and removal method for endoscopic images based on brightness classification. It can effectively detect the specular regions in endoscopic images of different brightness and can improve the operating efficiency of the algorithm while restoring the texture structure information of the high-resolution image. In addition to achieving image brightness classification and enhancing the brightness component of low-brightness images, this method also includes two new steps: In the highlight detection phase, the adaptive threshold function that changes with the brightness of the image is used to detect absolute highlights. During the highlight recovery phase, the priority function of the exemplar-based image inpainting algorithm was modified to ensure reasonable and correct repairs. At the same time, local priority computing and adaptive local search strategies were used to improve algorithm efficiency and reduce error matching. The experimental results show that compared with the other state-of-the-art, our method shows better performance in terms of qualitative and quantitative evaluations, and the algorithm efficiency is greatly improved when processing high-resolution endoscopy images.

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A Global Optimization Method for Specular Highlight Removal From a Single Image

Cited by 25 publications

References 40 publications

An Effective Algorithm for Specular Reflection Image Enhancement

An Effective Algorithm for Specular Reflection Image Enhancement

Two Stage Classification with CNN for Colorectal Cancer Detection

Specular Reflections Detection and Removal for Endoscopic Images Based on Brightness Classification

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