Considering that the existing infrared and visible image fusion methods do not entirely consider the information differences between different modalities and within the same modalities and the fusion image exhibit problems such as loss of detailed texture information and low contrast, this paper proposes an infrared and visible image fusion method based on a dual-channel generative adversarial network. The generation and identification networks are trained through confrontation, and the trained generation network is used as the final image fusion model. The fusion model uses dual-channel to extract features from infrared and visible images to retain more cross-modal information. Furthermore, a self-attention mechanism is introduced to enhance the input features and improve feature-richness in the modal to strengthen the global dependence of feature pixels. The experimental results on the TNO public dataset show that compared with the existing image fusion methods, the fused image obtained by the method has a higher contrast and richer detailed texture than the image obtained using existing image fusion methods. The fused image can efficiently fit human visual perception and performs well across a range of evaluation metrics.