Generative adversarial network has received widespread attention in the field of infrared and visible image fusion,but single-path fusion is prone to lose shallow information and the ability to branch feature extraction is limited. This paper proposes a fusion method for infrared and visible images based on multi-path generative adversarial net works. In the generator,three input paths are constructed using the source images and the results of guided image filter to extract more source image feature information to obtain detailed and rich fused images.Then,the convolutional layer adds an extract mask attention module to improve the efficiency of extracting significant information.In addition,dense connections and residual connections are introduced for improving the efficiency of feature transmission and obtaining more important feature information of the source image.In the discriminator,to avoid the modal imbalance problem of losing contrast information in a single discriminator network,dual discriminators are used to estimate the regional distribution of infrared and visible light images.Experiments are performed on the TNO dataset,and the experimental results show that the proposed algorithm achieves the best results in four of the five objective evaluation indicators and outperforms most mainstream algorithms.In terms of subjective evaluation,the proposed algorithm retained more texture detail information and has better visual effects.