Weighted Schatten p-Norm Minimization for Image Denoising and Background Subtraction

doi:10.1109/TIP.2016.2599290

CORC > 自动化研究所 > 中国科学院自动化研究所 > 精密感知与控制研究中心 > 人工智能与机器学习

	Weighted Schatten p-Norm Minimization for Image Denoising and Background Subtraction
	Xie, Yuan1,2 ; Gu, Shuhang 3; Liu, Yan 3; Zuo, Wangmeng 4; Zhang, Wensheng2 ; Zhang, Lei 3; Yuan Xie
刊名	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2016-10-01
卷号	25 期号:10 页码:4842-4857
关键词	Low Rank Weighted Schatten P-norm Low-level Vision
DOI	10.1109/TIP.2016.2599290
文献子类	Article
英文摘要	Low rank matrix approximation (LRMA), which aims to recover the underlying low rank matrix from its degraded observation, has a wide range of applications in computer vision. The latest LRMA methods resort to using the nuclear norm minimization (NNM) as a convex relaxation of the nonconvex rank minimization. However, NNM tends to over-shrink the rank components and treats the different rank components equally, limiting its flexibility in practical applications. We propose a more flexible model, namely, the weighted Schatten p-norm minimization (WSNM), to generalize the NNM to the Schatten p-norm minimization with weights assigned to different singular values. The proposed WSNM not only gives better approximation to the original low-rank assumption, but also considers the importance of different rank components. We analyze the solution of WSNM and prove that, under certain weights permutation, WSNM can be equivalently transformed into independent non-convex l(p)-norm subproblems, whose global optimum can be efficiently solved by generalized iterated shrinkage algorithm. We apply WSNM to typical low-level vision problems, e.g., image denoising and background subtraction. Extensive experimental results show, both qualitatively and quantitatively, that the proposed WSNM can more effectively remove noise, and model the complex and dynamic scenes compared with state-of-the-art methods.
WOS关键词	RANK MINIMIZATION ; MATRIX COMPLETION ; MISSING DATA ; APPROXIMATION ; FACTORIZATION ; RESTORATION ; ALGORITHMS ; SIGNALS
WOS研究方向	Computer Science ; Engineering
语种	英语
WOS记录号	WOS:000382677700008
资助机构	Hong Kong Scholars Program ; HK RGC GRF(PolyU 5313/13E) ; National Natural Science Foundation of China(61402480 ; 61432008 ; 61472423 ; 61502495 ; 41401383 ; 61373077)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/12447]
专题	精密感知与控制研究中心_人工智能与机器学习
通讯作者	Yuan Xie
作者单位	1.Hong Kong Polytech Univ, Dept Comp, Visual Comp Lab, Hong Kong, Hong Kong, Peoples R China 2.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China 3.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China 4.Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China
推荐引用方式 GB/T 7714	Xie, Yuan,Gu, Shuhang,Liu, Yan,et al. Weighted Schatten p-Norm Minimization for Image Denoising and Background Subtraction[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2016,25(10):4842-4857.
APA	Xie, Yuan.,Gu, Shuhang.,Liu, Yan.,Zuo, Wangmeng.,Zhang, Wensheng.,...&Yuan Xie.(2016).Weighted Schatten p-Norm Minimization for Image Denoising and Background Subtraction.IEEE TRANSACTIONS ON IMAGE PROCESSING,25(10),4842-4857.
MLA	Xie, Yuan,et al."Weighted Schatten p-Norm Minimization for Image Denoising and Background Subtraction".IEEE TRANSACTIONS ON IMAGE PROCESSING 25.10(2016):4842-4857.