摘要： This paper establishes a novel fusion scheme for infrared (IR) and visual (VI) images via low-rank representation (LRR), total variation (TV) model and simplified dual channel pulse coupled neural network (S-DPCNN) to effectively extract the major and salient information, which address some problems in existing fusion methods low-contrasting such as blurry edge, heterogeneous and information redundancy. The first step of the proposed method is to extract the valuable features of the IR images based on frequency tuned based- LRR (FT-LRR) algorithm aims to separate the corresponding salient regions and backgrounds. Furthermore, we adopt a choose-maximum rule to retain the significance information of the source images to the maximum extent for the salient region. For the background, IR and VI images are decomposed into a low-pass coefficient and a series of high-pass coefficients using non-subsampled shearlet transform (NSST). Then, the TV model is utilized to fuse the low-pass coefficient, in the meanwhile, the modified average gradient (MAG) is used to stimulate the S-DPCNN which aims to fuse high-pass coefficients. The fused background is obtained by taking inverse NSST. Finally, the robust fused image is generated by adding the fused salient region and background. Large amounts of experiment results and metrics demonstrate that the proposed framework exhibits good visual performance and has obvious superiorities over other state-of-the-art methods in both subjective and objective evaluation.