An efficient learned dictionary and its application to non-local denoising

This paper proposes a new learned dictionary for sparse representation of given data and suggests a way to apply it to non-local denoising. First, a quad-tree structure is efficiently embedded into a sparse dictionary model. This enables the dictionary to discover the complex structures in the given data and to be easily employed to high dimensional data. Besides, we propose a joint 3-D operation to exploit the correlations among the similar blocks, as the non-local denoising model assumes that there exist mutually similar blocks in nature images. This 3-D operation is achieved by a simple concatenation of the similar patches to a single vector and training the proposed dictionary on it. The experimental results indicate that our approach is competitive with several well known denoising techniques in terms of both PSNR and visual quality.