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

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 mini...

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Bibliographic Details
Published in:IEEE transactions on image processing 2016-10, Vol.25 (10), p.4842-4857
Main Authors: Xie, Yuan, Gu, Shuhang, Liu, Yan, Zuo, Wangmeng, Zhang, Wensheng, Zhang, Lei
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Computational modeling
Electronic mail
Image denoising
Low rank
low-level vision
Mathematical analysis
Minimization
Noise reduction
Optimization
Sparse matrices
Subtraction
weighted Schatten p-norm
Wireless sensor networks
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