Guaranteed matrix recovery using weighted nuclear norm plus weighted total variation minimization

This work presents a general framework regarding the recovery of matrices equipped with hybrid low-rank and local-smooth properties from just a few measurements consisting of linear combinations of the matrix entries. Concretely, we consider the problem of robust low-rank matrix recovery using Weighted Nuclear Norm plus Weight Total Variation (WNNWTV) minimization. First of all, based on a new restricted isometry property, we prove that the WNNWTV method possesses an error bound consisting of a low-rank approximation term, a total variation approximation term, and an observation error term. It should be noted that although there are many models considering both properties, there are very few recoverable error theories about such models. Specifically, the theoretical error bound provides an automatic mechanism to reducing regularization parameters with no need for cross-validation while keeping almost the same selection result with commonly used cross-validation technique. Subsequently, the proposed method is reformulated into a regularized unconstrained problem, and we study its optimization aspects in detail based on the Alternating Direction Method of Multipliers (ADMM). Extensive experiments on synthetic data and two applications, i.e. hyperspectral image recovery and dynamic magnetic resonance imaging recovery verified our theories and proposed algorithms.