An infrared and visible image fusion algorithm, based on a dual-discriminator generative adversarial network, is proposed to address issues, such as the insufficient extraction of global and multiscale features and the imprecise extraction of key information, in existing infrared and visible image fusion algorithms. First, a generator combines convolution and self-attention mechanisms to capture multiscale local and global features. Second, the attention mechanism is combined with skip connections to fully utilize multiscale features and reduce information loss during the downsampling process. Finally, two discriminators guide the generator to focus on the salient targets of the infrared images and background texture information of visible-light images, allowing the fused image to retain more critical information. Experimental results on the public multi-scenario multi-modality (M³FD) and multi-spectral road scenarios (MSRS) datasets show that compared with the baseline algorithms, the results of the six evaluation metrics improved significantly. Specifically, the average gradient (AG) increased by 27.83% and 21.06% on the two datasets, respectively, compared with the second-best results. The fusion results of the proposed algorithm are rich in detail and exhibit superior visual effects.