Sparse denoising with learned composite structured dictionaries

In the sparse representation field recent studies using composite dictionaries have shown encouraging results in performing noise removal. In this paper we look at dictionary composition in the particular case of dictionaries structured as a union of orthonormal bases. Our study focuses on denoising performance, providing new algorithms that outperform existing solutions, and also speed, resulting in different algorithms that execute a lot faster with a negligible denoising penalty.