Aiming at the problem that a single traditional discriminator Generative Adversarial Network (GAN) tends to ignore the brightness information and edge information of infrared light in the outdoor scene at night,a fusion algorithm of infrared and visible images based on attention mechanism and dual discriminators is proposed.Firstly,in order to pertinently obtain target information of infrared images and background texture information of visible images,a channel attention mechanism is introduced into the generator network.Secondly, the GAN with two discriminators is used,and a new discriminator input is designed to improve the training stability while better preserving the source image information.Finally,the loss functions are set as adversarial loss,structural similarity loss and gradient loss to constrain the discriminator for generating fusion images with rich details.The experimental results on the TNO dataset show that the fusion image obtained by this algorithm has more significant gradient changes and clearer edges,which is more in line with human visual effects.