To improve the visual effect and time efficiency of infrared and visible image fusion, the source images were decomposed into a series of high and low frequency sub-bands with the same size and different scales by Finite Discrete Shearlet Transform (FDST). Then, in the fusion process of low frequency sub-bands, the improved spatial frequency was used as the input excitation of Pulse Coupled Neural Network(PCNN), and the link strength was dynamically adjusted to change adaptively according to the image features, which fully preserved the feature information of image contour and edge. In the fusion of high frequency sub-band, the strategy of regional average energy contrast was used to fuse, which highlighted the information such as texture and details as much as possible. Finally, the image with clear background and prominent target was reconstructed with the processed high and low frequency sub-bands by using FDST inverse transform. The experimental results show that the improved fusion method can present the background and target in the image more clearly and comprehensively, compared with other algorithms, and performs the best subjective and objective indicators with the highest operation efficiency.