FusionGAN: A generative adversarial network for infrared and visible image fusion

Jiayi Ma, Wei Yu, Pengwei Liang, Chang Li, Junjun Jiang

Abstract: Highlights•We propose a new IR/VIS fusion method based on Generative Adversarial Networks.•It can keep both the thermal radiation and the texture details in the source images.•It is an end-to-end model and does not need to design fusion rules manually.•Our results look like sharpened IR images with highlighted target and abundant textures.•We generalize it to fuse images with different resolutions like thermal pan-sharpening.