The Experts below are selected from a list of 13836 Experts worldwide ranked by ideXlab platform
Yuzhen Niu - One of the best experts on this subject based on the ideXlab platform.
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Visually Consistent Color Correction for Stereoscopic Images and Videos
IEEE Transactions on Circuits and Systems for Video Technology, 2020Co-Authors: Yuzhen Niu, Xiaohua Zheng, Tiesong Zhao, Junhao ChenAbstract:In stereoscopic 3D (S3D) Color Correction, visual inconsistency is a common problem that leads to perceptual quality degradations. In this paper, we propose an S3D image/video Color Correction strategy that resolves global, local, and temporal Color discrepancies simultaneously. We achieve the image-based S3D Color Correction by three steps: a coarse-grain Color Correction for global Color matching, a fine-grain Color Correction to further improve both global and local Color consistencies, and a guided filtering process to guarantee the structural consistency before and after Color Correction. In addition, we extend the above strategy to S3D and multiview video Color Correction. To achieve temporal consistency between successive video frames, we develop an improved histogram matching within a sliding window on time axis. In our method, the mapping functions for each Color channel change gradually following the video stream to avoid abrupt temporal changes in Colors. The experimental results demonstrate that the proposed strategy outperforms the state-of-the-art Color Correction algorithms for images and videos.
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Matting-Based Residual Optimization for Structurally Consistent Image Color Correction
IEEE Transactions on Circuits and Systems for Video Technology, 2020Co-Authors: Yuzhen Niu, Tiesong Zhao, Liu Pengyu, Fan YuanyuanAbstract:Image Color Correction aims to eliminate Color differences between images, especially for Color consistency in panoramic or stereoscopic images. Nowadays, the global Color Correction methods cannot correct local Color differences, while the local Color Correction methods usually lead to structural inconsistency between local regions and image clarity reduction. To address these problems, we propose a matting-based residual optimization for structurally consistent image Color Correction (MROC). Inspired by the residual image, we formulate the image Color Correction problem as the optimization of a residual image between the input target image and the resulting image. The residual image is initialized and improved by the soft matting method with a closed-form solution. Besides, a data term is introduced to identify those pixels with higher Color and structural consistencies and preserve these pixels during optimization. The whole computational infrastructure operates at the pixel level to correct local Color differences while maintaining image clarity. Experimental results demonstrate that the performance of the proposed MROC method is superior to the state-of-the-art image Color Correction methods. Furthermore, the proposed matting-based residual optimization can also be incorporated in a variety of Color Correction methods, with enhanced outcomes justified by a group of image quality assessment metrics.
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Color Correction for Stereoscopic Images Based on Gradient Preservation
Advances in Intelligent Systems and Computing, 2019Co-Authors: Liu Pengyu, Yuzhen Niu, Junhao Chen, Yiqing ShiAbstract:Color Correction can eliminate the Color difference between similar images in image stitching and 3D video reconstruction. The result images generated by the local Color Correction algorithms usually show structure inconsistency problem with the input images. In order to solve this problem, we propose a structure consistent Color Correction algorithm for stereoscopic images based on gradient preservation. This method can not only eliminate Color difference between reference and target images, but also optimize structure between the input target image and the result image. Firstly, the algorithm extracts the structure information of the target image and style information of the reference image using the SIFT algorithm and generates the structure image and the pixel matching image. Then an initial result image is generated by local pixel mapping. Finally the initial result image is iteratively optimized by the gradient preserving algorithm. The experimental results show that our algorithm can not only optimize the structure inconsistency, but also effectively process image pairs with large Color difference.
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Image Quality Assessment for Color Correction Based on Color Contrast Similarity and Color Value Difference
IEEE Transactions on Circuits and Systems for Video Technology, 2018Co-Authors: Yuzhen Niu, Zhang Haifeng, Wenzhong GuoAbstract:Color Correction plays an important role in the image processing field. But substantial research on the assessment of Color Correction is still insufficient. In this paper, we present an image quality assessment metric for Color Correction. It assesses the Color consistency between the reference and target/result images of Color Correction according to their Color contrast similarity and Color value difference. Both the average difference and difference span are considered during the assessment. To compensate for the scene difference between the reference and target/result images of Color Correction, we propose to use an image registration algorithm to build their matching relationship, upon which a matching image is built. The matching image has the same scene as the target image and the same Color feature as the reference image, and thus the matching image is regarded as the real reference image of our Color Correction assessment. Furthermore, we combine a confidence map of the matching image and a saliency map of the target/result image as a weighting map for assessment, which helps to improve the consistency between the objective and subjective assessment results. The experimental results show that our Color Correction assessment metric has better correlation, accuracy, and monotonicity with users’ subjective scores than 19 state-of-the-art metrics.
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IC3D - Color Correction for stereoscopic image based on matching and optimization
2017 International Conference on 3D Immersion (IC3D), 2017Co-Authors: Xiaohua Zheng, Yuzhen Niu, Junhao Chen, Chen YuzhongAbstract:Stereoscopic 3D (S3D) image Color Correction is a major issue in the field of image processing. However, existing Color Correction algorithms have limitations. Global Color Correction algorithms cannot handle local Color discrepancies, and local Color Correction algorithms are sensitive to matching quality between reference and target images. In this study, we propose an S3D image Color Correction algorithm that combines global and local Color information to correct Color discrepancies between S3D images. Sparse feature matching usually generates only a few matching features, producing error Correction results in some local regions. Our algorithm uses dense stereo matching and global Color Correction algorithms to initialize Color values, and improves the local Color smoothness and global Color consistency of the resulting image, while maintaining the initial Color in that image as much as possible. Experimental results show that our algorithm performs better than do five state-of-the-art Color Correction algorithms.
Steve Hullfish - One of the best experts on this subject based on the ideXlab platform.
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Primary Color Correction: Tonal Range Primer
The Art and Technique of Digital Color Correction, 2012Co-Authors: Steve HullfishAbstract:Color Correction is generally broken down into two distinct processes: primary and secondary Color Correction. These two processes will probably always be referred to as two distinct processes, but the technology itself is starting to change the perception of how and why these two processes are used and when the Colorist moves from one process to another. Primary Color Correction is the process of setting the overall tone, contrast, and Color balance of the image. Secondary Color Correction is an additional step that refines the image in specific geographical regions of the image or in specific Color vectors of the image.
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Primary Color Correction: Tonal Correction Tools
The Art and Technique of Digital Color Correction, 2012Co-Authors: Steve HullfishAbstract:Across the range of products, there are lots of tools. Some of them have applications in altering tonal range; some are more commonly used to control the "Color" of the image, generically meaning that they'd be used to control hue and saturation, though they'd also have some affect on the tonal range as well. For tonal Corrections, almost every application that has Color Correction abilities has some slider or numerical controls to adjust brightness, contrast, black level, and gamma. This chapter describes the tonal Correction tools available in several of the applications and plug-ins for doing Color Correction. It also describes their respective strengths and weaknesses.
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The Art and Technique of Digital Color Correction
2008Co-Authors: Steve HullfishAbstract:Tonal Range Primer Tonal Range Tools Tonal Range Workshop Color primer Color Tools Color Workshop Using secondary Color Correction Spot Color Correction ? windows and vignettes Matching shots in sequence Telling the story Creating special looks Saving bad footage
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Secondary Color Correction Primer
The Art and Technique of Digital Color Correction, 2008Co-Authors: Steve HullfishAbstract:Although primary Color Correction affects the entire raster, secondary Color Correction is limited to specific geographic regions – for example, vignettes or windows – or specific Color vectors. Secondary Color Correction can also affect specific tonal regions, but these secondary regions are more specific than the shadows, midtones, and highlights that are used to qualify Corrections in primary Color Correction. As the tools have become more nonlinear, the distinctions between what is primary and what is secondary are beginning to blur.
Anya Hurlbert - One of the best experts on this subject based on the ideXlab platform.
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Color Correction using root polynomial regression
IEEE Transactions on Image Processing, 2015Co-Authors: Graham D Finlayson, Michal Mackiewicz, Anya HurlbertAbstract:Cameras record three Color responses ( $RGB$ ) which are device dependent. Camera coordinates are mapped to a standard Color space, such as XYZ—useful for Color measurement—by a mapping function, e.g., the simple $3\times 3$ linear transform (usually derived through regression). This mapping, which we will refer to as linear Color Correction (LCC), has been demonstrated to work well in the number of studies. However, it can map $RGB\text{s}$ to XYZs with high error. The advantage of the LCC is that it is independent of camera exposure. An alternative and potentially more powerful method for Color Correction is polynomial Color Correction (PCC). Here, the $R$ , $G$ , and $B$ values at a pixel are extended by the polynomial terms. For a given calibration training set PCC can significantly reduce the Colorimetric error. However, the PCC fit depends on exposure, i.e., as exposure changes the vector of polynomial components is altered in a nonlinear way which results in hue and saturation shifts. This paper proposes a new polynomial-type regression loosely related to the idea of fractional polynomials which we call root-PCC (RPCC). Our idea is to take each term in a polynomial expansion and take its $k$ th root of each $k$ -degree term. It is easy to show terms defined in this way scale with exposure. RPCC is a simple (low complexity) extension of LCC. The experiments presented in this paper demonstrate that RPCC enhances Color Correction performance on real and synthetic data.
Junhao Chen - One of the best experts on this subject based on the ideXlab platform.
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Visually Consistent Color Correction for Stereoscopic Images and Videos
IEEE Transactions on Circuits and Systems for Video Technology, 2020Co-Authors: Yuzhen Niu, Xiaohua Zheng, Tiesong Zhao, Junhao ChenAbstract:In stereoscopic 3D (S3D) Color Correction, visual inconsistency is a common problem that leads to perceptual quality degradations. In this paper, we propose an S3D image/video Color Correction strategy that resolves global, local, and temporal Color discrepancies simultaneously. We achieve the image-based S3D Color Correction by three steps: a coarse-grain Color Correction for global Color matching, a fine-grain Color Correction to further improve both global and local Color consistencies, and a guided filtering process to guarantee the structural consistency before and after Color Correction. In addition, we extend the above strategy to S3D and multiview video Color Correction. To achieve temporal consistency between successive video frames, we develop an improved histogram matching within a sliding window on time axis. In our method, the mapping functions for each Color channel change gradually following the video stream to avoid abrupt temporal changes in Colors. The experimental results demonstrate that the proposed strategy outperforms the state-of-the-art Color Correction algorithms for images and videos.
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Color Correction for Stereoscopic Images Based on Gradient Preservation
Advances in Intelligent Systems and Computing, 2019Co-Authors: Liu Pengyu, Yuzhen Niu, Junhao Chen, Yiqing ShiAbstract:Color Correction can eliminate the Color difference between similar images in image stitching and 3D video reconstruction. The result images generated by the local Color Correction algorithms usually show structure inconsistency problem with the input images. In order to solve this problem, we propose a structure consistent Color Correction algorithm for stereoscopic images based on gradient preservation. This method can not only eliminate Color difference between reference and target images, but also optimize structure between the input target image and the result image. Firstly, the algorithm extracts the structure information of the target image and style information of the reference image using the SIFT algorithm and generates the structure image and the pixel matching image. Then an initial result image is generated by local pixel mapping. Finally the initial result image is iteratively optimized by the gradient preserving algorithm. The experimental results show that our algorithm can not only optimize the structure inconsistency, but also effectively process image pairs with large Color difference.
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IC3D - Color Correction for stereoscopic image based on matching and optimization
2017 International Conference on 3D Immersion (IC3D), 2017Co-Authors: Xiaohua Zheng, Yuzhen Niu, Junhao Chen, Chen YuzhongAbstract:Stereoscopic 3D (S3D) image Color Correction is a major issue in the field of image processing. However, existing Color Correction algorithms have limitations. Global Color Correction algorithms cannot handle local Color discrepancies, and local Color Correction algorithms are sensitive to matching quality between reference and target images. In this study, we propose an S3D image Color Correction algorithm that combines global and local Color information to correct Color discrepancies between S3D images. Sparse feature matching usually generates only a few matching features, producing error Correction results in some local regions. Our algorithm uses dense stereo matching and global Color Correction algorithms to initialize Color values, and improves the local Color smoothness and global Color consistency of the resulting image, while maintaining the initial Color in that image as much as possible. Experimental results show that our algorithm performs better than do five state-of-the-art Color Correction algorithms.
Zhang Bao-zheng - One of the best experts on this subject based on the ideXlab platform.
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Color Correction of the video image obtained by angioscope.
Journal of biomedical optics, 1998Co-Authors: Fang Fang, Lin Mei-rong, Li Yingjie, Li Jia, Zhang Bao-zhengAbstract:In this article, a simple Color Correction method for the Color reproduction of image obtained by angioscope is reported. We present the method to obtain the matrices theoretically and experimentally, respectively. For the angioscope system, we suggest two matrices: an average Color Correction matrix A and a blood vessel Color Correction matrix A'. Using the two matrices, the images of several Color samples captured by an angioscope are processed, and their reproduced Colors are evaluated. With this method, the discrimination ability of the angioscope will be improved dramatically. © 1998 Society of Photo-Optical Instrumentation Engineers.
