Due to the conventional sparse representation (SR) redundant dictionar y is single and the parameter setting of pulse- coupled neural network (PCNN) model is complex.To solve the above problems,an infrared and visible image fusion algorithm based on the non-subsampled shearlet transform (NSST) is proposed.Firstly,the algorithm decomposes the source image into low-frequency subband and high-frequency subband through NSST.Then,the adapt ive sparse representation (ASR) model is used to merge the low-frequency sparse coefficients in the NSST domain.At the same time,a parameter-adaptive pulse-coupled neural network (PA-PCNN) model is used to fuse the corresponding high-frequenc y parts.Finally,the fused image is reconstructed by performing inverse NSST on the fused low-frequency and high-f requency bands.Experimental results demonstrate that the algorithm solves the "block effect" problem of the conventi onal SR model,overcomes the difficulty of setting free parameters in the conventional PCNN model,leading to state-of-th e-art results on both subjective vision and objective assessment.