Analysis-synthesis dictionary pair learning and patch saliency measure for image fusion

Abstract

In image fusion, Sparse Representation (SR)-based method has received extensive attention because of its excellent performance. In such method, the quality of over-complete dictionary and the fusion rules of SR coefficients are the most important factors that affect the final fusion quality. However, the traditional dictionary learning methods are usually designed based on Synthesis Sparse Representation (SSR), and this strategy ignores the complementary between SSR and Analysis Sparse Representation (ASR). To construct two dictionaries with powerful representation capability and discriminative capability, we integrate the complementary representation mechanisms of analysis and synthesis SR into our dictionary learning and image decomposition. Then we develop a novel dictionary learning and image decomposition algorithm for image fusion. Moreover, the traditional SR-based fusion method often adopts the simple principle of maximum absolute value in the fusion of SR coefficients, which leads to a fused result with poor visual quality. To this end, we propose to fuse the coding coefficients of the major structure and edge detail components according to the saliency measure of the corresponding patches. Experimental results show that the proposed method can better preserve the image information and improve the image contrast.