To improve the visual perception of fused images, a nonsubsampled shear wave transform (NSST) -based perception fusion method for infrared and visible images is proposed. First, the NSST is used to decompose the source image into high- and low-frequency components. Then, to improve image details, a parameter adaptive pulse coupled neural network is used to fuse high-frequency component images. Second, a Gaussian filter and a bilateral filter are used for multiscale transformation to fuse low-frequency component images, and low-frequency components are decomposed into multiscale texture details and edge features to capture more multiscale infrared spectral features. Finally, the inverse NSST is used to obtain the fused image. Experimental results show that the proposed method can not only improve the detail information of fusion image effectively, but also enhance the ability of infrared feature extraction to fit human visual perception.