Bridging the Invisible and Visible World: Translation between RGB and IR Images through Contour Cycle GAN

Abstract

Infrared Radiation (IR) images that capture the emitted IR signals from surrounding environment have been widely applied to pedestrian detection and video surveillance. However, there are not many textures that appeared on thermal images as compared to RGB images, which brings enormous challenges and difficulties in various tasks. Visible images cannot capture scenes in the dark and night environment due to the lack of light. In this paper, we propose a Contour GAN-based framework to learn the cross-domain representation and also map IR images with visible images. In contrast to existing structures of image translation that focus on spectral consistency, our framework also introduces strong spatial constraints, with further spectral enhancement by illuminance contrast and consistency constraints. Designating our method for IR and RGB image translation, it can generate high-quality translated images. Extensive experiments on near IR (NIR) and far IR (thermal) datasets demonstrate superior performance for quantitative and visual results.