Aiming at the difference characteristi cs between infrared image target information and visible images detail informati on,this paper introduce an improved pulse coupled neural network (PCNN) and prop ose a novel infrared and visible images fusion algorithm based on Non-subsampled Shearlet Transform (NSST).Firstly,we obtain the high and low frequency components by using the NSST multi-scale decomposition o f the strictly registered source images.Secondly,the low frequency components are fused by using the modified spa tial frequency as the external excitation of the PCNN,at the same time,the average gradient of low frequency co mponents are used to adjust the link strength adaptively.Moreover,for the high frequency components,we present a self-adaptive fusion rule algorithm based on local area variance and local area average gradient.Finally, this paper uses the NSST inverse transform method to fuse low and high frequency components to obtain a fused ima ge.Experimental results show that the proposed method of image fusion can effectively integrate important inf ormation in infrared and visible images,and the fusion effect is better than those of the general image fusion me thods.