Total variation regularized low-rank tensor decomposition with nonlocal for single image denoising

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	Total variation regularized low-rank tensor decomposition with nonlocal for single image denoising
	Li, Shengchuan 1; Wang YM(王艳美)2,3,5; Luo Q(罗琼)2,3,5; Wang, Kai 4; Han Z(韩志)2,5; Tang YD(唐延东)2,5
	2021
会议日期	July 15-19, 2021
会议地点	Xining, China
页码	533-537
英文摘要	Various noises in the image will reduce the quality of the image and seriously affect the processing of subsequent computer tasks. The recovery of single images is a more challenging problem than recovery of spectral images due to the lack of spectral information. In order to solve this problem, in this paper, we propose a method combining non-local self-similar priors and tensor decomposition to fully explore the inherent low-rank structure of a single image. Specifically, we use tucker decomposition to characterize the global self-similar patch of a single image. At the same time, we introduce anisotropic spatial-spectral total variation regularization to describe the segmented smooth structure in the image. In order to deal with the complex noise situation in the real scene. We model the noise in two parts, one part is sparse spot noise, and the other part is ubiquitous noise. Then we use the augmented Lagrange multiplier method to solve it. Experiments have proved that the introduction of non-local self-similar priors is crucial to the denoising problem of a single image. The proposed method is superior to all comparison methods.
源文献作者	IEEE Robotics and Automation Society (RA) ; Shanghai Jiao Tong University ; Shenzhen Institute of Advanced Technology (SIAT)
产权排序	2
会议录	2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021
会议录出版者	IEEE
会议录出版地	New York
语种	英语
ISBN号	978-1-6654-3678-6
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/29684]
专题	沈阳自动化研究所_机器人学研究室
通讯作者	Luo Q(罗琼)
作者单位	1.State Grid Liaoning Electric Power Research Institute, Shenyang, China 2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China 3.University of Chinese Academy of Sciences, Beijing 100049, China 4.State Grid Shandong Electric Power Company, Shandong, China 5.Shenyang Institute of Automation, Chinese Academy of Sciences, State Key Laboratory of Robotics, Shenyang, China
推荐引用方式 GB/T 7714	Li, Shengchuan,Wang YM,Luo Q,et al. Total variation regularized low-rank tensor decomposition with nonlocal for single image denoising[C]. 见:. Xining, China. July 15-19, 2021.