There is complementarity between visible light images and infrared images in application, and fusion can improve the perceptual performance of images. Existing methods for fusion have poor image quality and cannot meet the application requirements of fusion images. Therefore, a visible light and infrared image fusion method based on generative adversarial networks is proposed. Build a visible light and infrared image fusion network framework using generative adversarial networks, and based on this, design a generator structure; Through brightness perception, adjustment, feature map extraction, and feature map fusion, visible light and infrared fusion images are generated. The discriminator network structure is determined, and the image fusion network is trained to make the distance between the true distribution and the false distribution approach zero until the constraint function - loss function - reaches the minimum value. The visible light image and infrared image to be fused are input into the trained network model, and the output result is the visible light and infrared fusion image. The experimental results show that the maximum mutual information value of the fusion image obtained by the proposed method is 17, the maximum average gradient value is 4, and the maximum edge strength value is 28.6, which fully confirms that the quality of the fusion image obtained by the proposed method is better.