On the global-local dichotomy in sparsity modeling

The traditional sparse modeling approach, when applied to inverse problems with large data such as images, essentially assumes a sparse model for small overlapping data patches and processes these patches as if they were independent from each other. While producing state-of-the-art results, this methodology is suboptimal, as it does not attempt to model the entire global signal in any meaningful way—a nontrivial task by itself. In this paper we propose a way to bridge this theoretical gap by constructing a global model from the bottom-up. Given local sparsity assumptions in a dictionary, we show that the global signal representation must satisfy a constrained underdetermined system of linear equations, which forces the patches to agree on the overlaps. Furthermore, we show that the corresponding global pursuit can be solved via local operations. We investigate conditions for unique and stable recovery and provide numerical evidence corroborating the theory.