Image fusion based on group sparse representation

Abstract

Sparse representation based image fusion has been widely studied recently. However, it’s not popular in some fields for the high time complexity. In this paper, a new image fusion method based on group sparse representation is proposed to overcome this problem. The K-SVD method is utilized to get the sparse representation of the source images. Therefore, it is necessary to find the best size of the group according to its property about time consuming. And there is no need to sparse all the patches once but to sparse some groups simultaneously. Because every group image vectors sparse representation is unique from the others, using the parallel-processing strategy can reduce the time badly. Besides, all dictionaries are learned from local source image vectors, so the quality of the results fused by the group sparse representation method will be better than those fused by the normal sparse representation methods. Compared with four types of state-of-the-art algorithms, the proposed method has the excellent fusion performance in experiments.