Nonlocal image denoising via adaptive tensor nuclear norm minimization

doi:10.1007/s00521-015-2050-5

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	Nonlocal image denoising via adaptive tensor nuclear norm minimization
	Zhang, Chenyang 1; Hu, Wenrui2 ; Jin, Tianyu 1; Mei, Zhonglei 1; Chenyang Zhang
刊名	NEURAL COMPUTING & APPLICATIONS
	2018
卷号	29 期号:1 页码:3-19
关键词	Nonlocal Self-similarity Low-rank Tensor Estimation Singular-value Thresholding Tensor Nuclear Norm
DOI	10.1007/s00521-015-2050-5
文献子类	Article
英文摘要	Nonlocal self-similarity shows great potential in image denoising. Therefore, the denoising performance can be attained by accurately exploiting the nonlocal prior. In this paper, we model nonlocal similar patches through the multi-linear approach and then propose two tensor-based methods for image denoising. Our methods are based on the study of low-rank tensor estimation (LRTE). By exploiting low-rank prior in the tensor presentation of similar patches, we devise two new adaptive tensor nuclear norms (i.e., ATNN-1 and ATNN-2) for the LRTE problem. Among them, ATNN-1 relaxes the general tensor N-rank in a weighting scheme, while ATNN-2 is defined based on a novel tensor singular-value decomposition (t-SVD). Both ATNN-1 and ATNN-2 construct the stronger spatial relationship between patches than the matrix nuclear norm. Regularized by ATNN-1 and ATNN-2 respectively, the derived two LRTE algorithms are implemented through the adaptive singular-value thresholding with global optimal guarantee. Then, we embed the two algorithms into a residual-based iterative framework to perform nonlocal image denoising. Experiments validate the rationality of our tensor low-rank assumption, and the denoising results demonstrate that our proposed two methods are exceeding the state-of-the-art methods, both visually and quantitatively.
WOS关键词	MATRIX COMPLETION ; ITERATIVE REGULARIZATION ; DECOMPOSITION ; ALGORITHM ; OPTIMIZATION ; RESTORATION ; FRAMEWORK ; SHRINKAGE
WOS研究方向	Computer Science
语种	英语
WOS记录号	WOS:000422933800002
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/12260]
专题	精密感知与控制研究中心_精密感知与控制
通讯作者	Chenyang Zhang
作者单位	1.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Chenyang,Hu, Wenrui,Jin, Tianyu,et al. Nonlocal image denoising via adaptive tensor nuclear norm minimization[J]. NEURAL COMPUTING & APPLICATIONS,2018,29(1):3-19.
APA	Zhang, Chenyang,Hu, Wenrui,Jin, Tianyu,Mei, Zhonglei,&Chenyang Zhang.(2018).Nonlocal image denoising via adaptive tensor nuclear norm minimization.NEURAL COMPUTING & APPLICATIONS,29(1),3-19.
MLA	Zhang, Chenyang,et al."Nonlocal image denoising via adaptive tensor nuclear norm minimization".NEURAL COMPUTING & APPLICATIONS 29.1(2018):3-19.