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Non-Convex Weighted Schatten p-Norm Minimization based ADMM Framework for Image Restoration

Abstract · Apr 24, 2017 07:02 ·

admm nnm minimization nuclear rank schatten restoration norm cs-cv

Arxiv Abstract

  • Zhiyuan Zha
  • Xinggan Zhang
  • Yu Wu
  • Qiong Wang
  • Lan Tang

Since the matrix formed by nonlocal similar patches in a natural image is of low rank, the nuclear norm minimization (NNM) has been widely used for image restoration. However, NNM tends to over-shrink the rank components and treats the different rank components equally, thus limits its capability and flexibility. This paper proposes a new approach for image restoration based ADMM framework via non-convex weighted Schatten $p$-norm minimization (WSNM). To make the proposed model tractable and robust, we have developed the alternative direction multiplier method (ADMM) framework to solve the proposed non-convex model. Experimental results on image deblurring and image inpainting have shown that the proposed approach outperforms many current state-of-the-art methods in both of PSNR and visual perception.

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