Discriminator guided visible-to-infrared image translation

Abstract

This paper proposes a discriminator-guided visible-to-infrared image translation algorithm based on a generative adversarial network and designs a multi-scale fusion generative network. The generative network enhances the perception of the image’s fine-grained features by fusing features of different scales in the channel direction. Meanwhile, the discriminator performs the infrared image reconstruction task, which provides additional infrared information to train the generator. This enhances the convergence efficiency of generator training through soft label guidance generated through knowledge distillation. The experimental results show that compared to the existing typical infrared image generation algorithms, the proposed method can generate higher-quality infrared images and achieve better performance in both subjective visual description and objective metric evaluation, and that it has better performance in the downstream tasks of the template matching and image fusion tasks.