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

@article{Xie2016WeightedS,
  title={Weighted Schatten  \$p\$ -Norm Minimization for Image Denoising and Background Subtraction},
  author={Yuan Xie and Shuhang Gu and Yan Liu and Wangmeng Zuo and Wensheng Zhang and Lei Zhang},
  journal={IEEE Transactions on Image Processing},
  year={2016},
  volume={25},
  pages={4842-4857}
}
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… CONTINUE READING
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