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In this paper, we propose a reweighted low-rank matrix recovery method and demonstrate its application for robust image restoration. In the literature, principal component pursuit solves low-rank matrix recovery problem via a convex program of mixed nuclear norm and $\ell_{1}$ norm. Inspired by reweighted $\ell_{1}$ minimization for sparsity enhancement, we propose reweighting singular values...
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