This study proposes a novel fusion method that combines a pulse-coupled neural network (PCNN) and guided filtering to solve the issues of lacking details and virtual shadows in infrared and visible images. First, to eliminate the additional noise caused by the multi-impulse of a pixel in firing time matrix T, the traditional PCNN model is improved by simplifying its structure and adding restraint items into the pulse generating unit. Second, using original images as input, guided filtering is utilized to improve T with more edge details and salient information. Finally, based on the modified T, the weight fusion rule is adopted to obtain the fusion image. Following the firing mechanism analysis of the PCNN model, a new parameter setting method combining constraints is proposed to reduce the model's parameter setting complexity. Experimental results show that the proposed method provides efficient, satisfactory, and well-detailed fusion results, and obvious virtual shadows scarcely appear in the fusion image. Additionally, the cross entropy and space frequency indexes of the results are superior to those of other current fusion methods.