ICLR Poster Learning Sparse and Low-Rank Priors for Image Recovery via Iterative Reweighted Least Squares Minimization

In-Person Poster presentation / poster accept

Learning Sparse and Low-Rank Priors for Image Recovery via Iterative Reweighted Least Squares Minimization

Stamatios Lefkimmiatis · Iaroslav Koshelev

MH1-2-3-4 #128

Keywords: [ IRLS ] [ recurrent networks ] [ Low-rank ] [ optimization ] [ inverse problems ] [ sparsity ] [ Optimization ]

[ Abstract ]

[ Poster] [ OpenReview]

Abstract: In this work we introduce a novel optimization algorithm for image recovery under learned sparse and low-rank constraints, which are parameterized with weighted extensions of the $\ell_p^p$-vector and $\mathcal{S}_p^p$ Schatten-matrix quasi-norms for $0\!

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