Weighted Schatten p-Norm Minimization for Image Denoising and Background Subtraction
Xie, Yuan1,2; Gu, Shuhang3; Liu, Yan3; Zuo, Wangmeng4; Zhang, Wensheng2; Zhang, Lei3; Yuan Xie
2016-10-01
发表期刊IEEE TRANSACTIONS ON IMAGE PROCESSING
卷号25期号:10页码:4842-4857
文章类型Article
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.
关键词Low Rank Weighted Schatten P-norm Low-level Vision
WOS标题词Science & Technology ; Technology
DOI10.1109/TIP.2016.2599290
关键词[WOS]RANK MINIMIZATION ; MATRIX COMPLETION ; MISSING DATA ; APPROXIMATION ; FACTORIZATION ; RESTORATION ; ALGORITHMS ; SIGNALS
收录类别SCI
语种英语
项目资助者Hong Kong Scholars Program ; HK RGC GRF(PolyU 5313/13E) ; National Natural Science Foundation of China(61402480 ; 61432008 ; 61472423 ; 61502495 ; 41401383 ; 61373077)
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000382677700008
引用统计
被引频次:28[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符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
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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.
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