In order to further reduce the spectral and spatial distortion of the fused images from multispectral and panchromatic images, and improve the fusion quality, this paper proposes a particle swarm optimization pulse coupled neural network algorithm for multispectral and panchromatic image fusion. Based on the fusion framework of Principal Component Analysis and Non-Subsampled Contourlet Transform, the algorithm uses detail injection fusion method in low-frequency coefficient fusion process to reduce unnecessary information injection and improve spectral preservation. When fusing high-frequency coefficients, a simplified pulse coupled neural network with parameter adaptation is used to calculate the fusion weights, and the corresponding parameters that can obtain the best fusion quality are obtained by global search based on particle swarm optimization algorithm to improve the completeness and clarity of spatial information. The feasibility of the proposed algorithm is verified through three sets of experiments, and compared with existing and classic fusion algorithms. The experiments show that the proposed fusion algorithm has SAM around 0.1 and Q above 0.9 in all three sets of experiments. The experimental results show that the proposed algorithm can not only effectively improve the fusion quality of panchromatic and multispectral images, but also robust, and has the best fusion performance in comparison experiments.