A new framework for designing incoherent sparsifying dictionaries

Abstract

This paper deals with designing incoherent sparsifying dictionaries. A new framework is proposed, in which the sparse representation error and mutual coherence are embedded. An alternating minimization method is developed for solving the optimal dictionary problem. One of the significant features of the proposed approach is that the dictionary is directly updated with each atom being normalized. A gradient-based algorithm is derived for this purpose. Experiments are carried out and the results show that the proposed approach outperforms some prevailing ones in terms of minimizing sparse representation error and mutual coherence.