Aiming at the feature of infrared and vision images，a new fusion algorithm which combines nonsubsampled shearlet transform (NSST)with adaptive pulse coupled neural network (PCNN)is presented.For the low-frequency sub-band coefficients，a fusion rule which combines local variance with a Gaussian weight distribution matrix after NSST transform with variance matching is used.For the high-frequency sub-band coefficients，an improved spatial frequency as the input of the PCNN is used，and the improved sum of Laplace energy as the PCNN link strength is used.The high-frequency sub-band coefficients are selected by using the global coupling and pulse synchronization of PCNN，and finally fusion results are obtained by inverse NSST transform.The experiment results show that compared to the traditional image fusion algorithms，the proposed algorithm achieves better results in the subjective visual and also improves the objective criteria in some extent.