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

We introduce a novel optimization algorithm for image recovery under learned sparse and low-rank constraints, which we parameterize as weighted extensions of the $\ell_p^p$-vector and $\mathcal S_p^p$ Schatten-matrix quasi-norms for $0\!