Aiming at the traditional infrared and visible image fusion algorithms have problems in the edge information missing and the target feature being not prominent enough,et al,a novel infrared and visible image fusion algorithm based on optimized pulse coupled neural network(PCNN) and region feature guided rule is proposed.Firstly,non-subsampled shearlet transform(NSST) is applied to infrared and visible images to obtain the corresponding low-frequency components and high-frequency components.Secondly,the low-frequency components are fused by using the fusion rules based on the optimized PCNN model.Moreover,for the high-frequency components,use the feature of image,such as region energy,improved spatial frequency and region variance matching degree,et al,an adaptive threshold of region variance matching degree and new regulator factors are proposed,thus the region feature guided rule is constructed to fuse the high-frequency components.Finally,the fused image is obtained by inverse NSST of the low-frequency and high-frequency fused components.Experimental results show that the proposed algorithm can effectively integrate dominant information in infrared and visible images,and has the obvious advantages in subjective vision and objective index.