Infrared and Visible Image Fusion via Multi-discriminators Wasserstein Generative Adversarial Network

Abstract

Generative adversarial network (GAN) has been widely applied to infrared and visible image fusion. However, the existing GAN-based image fusion methods only establish one discriminator in the network to make the fused image capture gradient information from the visible image, which may result in the loss of some infrared intensity information and texture information on the fused images. To solve this problem and improve the performance of GAN, we extend GAN to multiple discriminators and propose an end-to-end multi-discriminators Wasserstein generative adversarial network (MD-WGAN). In this framework, the fused image can preserve major infrared intensity and detail information from the first discriminator, and keep more texture information that existing in visible image from the second discriminator. We also design a texture loss function via local binary patterns to preserve more texture from visible image. The extensive qualitative and quantitative experiments show the advantages of our method compared with other state-of-the-art fusion methods.