Dual Reweighted Lp-Norm Minimization for Salt-and-pepper Noise Removal
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The robust principle analysis (RPCA), which aims to estimate underlying low rank and sparse structures from the degraded observation data, has a wide range of applications in computer vision. It is usually replaced by the component analysis model (PCP) in order to pursue the convex property, leading to the undesirable overshrink problem. In this paper, we propose a dual reweighted Lp-norm (DWLP) model with a more reasonable weighting rule and weaker powers, which greatly generalizes previous works and provides a better approximation to the rank minimization problem for original matrix as well as the L0-norm minimization problem for sparse noise. Moreover, an iterative reweighted algorithm is introduced to solve the proposed DWLP model by optimizing elements and weights alternatively. We then apply the DWLP model to remove salt-and-pepper noise by exploiting the image non-local self-similarity. Extensive experiments demonstrate that the proposed method outperforms other state-of-the-art methods in terms of both qualitative and quantitative evaluation. More precisely, our DWLP achieves about 6.814dB, 4.80dB, 3.142dB, 1.20d-B and 0.1dB improvements over the current WSNM-RPCA in average under salt-and-pepper noise densities 10
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