Abstract: In order to highlight the different features of different input images, a SPCNN model with automaticsetting parameter based on features is proposed, which is combined with sparse representation to fuse the multisource images. The fusion process has four steps. Firstly, the source images are decomposed into high frequency coefficients and low frequency coefficient by NSST. Each high frequency coefficient is fired by the SPCNN model with automaticset parameters based on the inherent characteristics, and the fused image is completed according to the total number of firing and the weighted fusion strategy. The low frequency coefficients are fused by a sparse representation. Finally, the fused image is reconstructed by inverse NSST. The experimental results show that the proposed method is superior to the other five classical methods and the fused image conforms to the human visual perception system, with clear structure and obvious details.

Key words: adaptiveparameters SPCNN, sparse representation, NSST, image fusion