Fusion of Infrared and Visible Images Based on Improved Generative Adversarial Networks

In order to improve the fusion quality of infrared and visible light images, enhance the visual effect of fused images, and solve the problems that traditional fusion methods need to manually set fusion rules and the background details of fused images are poorly preserved, this paper proposes an improved generative adversarial network that combines multi-scale information. The generator used in this method is a typical encoder and decoder structure, and the discriminator uses a dual discriminator to establish the confrontation relationship between the infrared source image, the visible light source image and the fusion image respectively. Before the source image is input to the encoder, multi-scale information is introduced through the Inception network, which effectively extracts the multi-scale features of the image, which ensures the subsequent improvement of the quality of the fusion image. In addition, the loss function is improved to retain more background details and highlight infrared feature information. The control experiment results show that the method in this paper obtains better fusion effect in subjective and objective evaluation.