Nonuniform image patch exemplars for low level vision

In this paper we propose the use of nonuniformly resized image patch exemplars for solving low level vision problems like denoising and super-resolution. While patch-based methods have been shown to be successful for several such applications, these methods have so far assumed uniform sizes for image patches. In this paper we address this restriction. We use an integral image representation for efficient computation of the matching cost for variable-sized patches. We show that nonuniform image patch exemplars are useful in improving classic techniques for nonlocal means-based denoising and example-based superresolution. We provide refinement cues to further improve the patch size estimation. This method can be adopted for a large number of related methods and applications due to its simplicity and generality.