A GAN-based visible and infrared image fusion algorithm

In this paper，we propose a new GAN-based end-to-end image fusion network (VIFGAN) for fusion of visible and infrared images. VIFGAN contains a generator and a discriminator. We added the DenseNet module to the generator，and this module can extract deeper features and details. We also propose a Two-way regulation loss function(TWR-Loss). The loss function considers both the radiation information and texture information in the image, which can make the network suitable for image fusion tasks of different spectrum combinations. The experimental results show that in the fusion task of visible light and infrared images, the proposed network has better fusion performance than the existing fusion algorithm, the visual effect of the fused image is better, and the extracted details are more abundant.