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Weighted Schatten p-Norm Minimization for Image Denoising and Background Subtraction | |
Xie, Yuan1,2![]() ![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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2016-10-01 | |
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 |
DOI | 10.1109/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 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/12447 |
专题 | 多模态人工智能系统全国重点实验室_人工智能与机器学习(杨雪冰)-技术团队 |
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 |
第一作者单位 | 精密感知与控制研究中心 |
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. |
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