Ordinary neural networks are difficult to generate infrared and visible light fusion images that conform to human vision,and the network model is complex and occupies too much memory.The existing Generative Adversarial Network (GAN) framework is improved.Firstly,deep convolution and point by point convolution are integrated into the generator,and a convolutional network with small convolution kernels is designed to reduce network parameters.Secondly,mask processing is applied to the source image to reduce the loss of source image information during feature extraction.Then,the processed image and the fused image obtained by the generator are jointly input into the discriminator to enhance the networks ability to retain source image information for visible light images.Finally,in the performance evaluation stage,the loss functions are set as gradient loss,adversarial loss,and content loss functions to constrain the fusion image to contain more background information of visible light images and target information of infrared images.The results of simulation experiments on the TNO image fusion dataset show that the proposed algorithm can obtain fused images with rich details and clear targets while reducing network complexity and operational parameters.