Traffic thermal infrared texture generation based on siamese semantic CycleGAN

Abstract Thermal infrared texture generation is a promising infrared imaging framework for various applications. A novel thermal infrared texture generation algorithm, based on siamese semantic CycleGAN (SS-CycleGAN), is proposed for thermal infrared systems. Different from traditional infrared simulation frameworks, SS-CycleGAN depends on no extra environmental information, such as air temperature, humidity and radiation properties of objects. In other words, visible images could be directly transformed into thermal infrared images like using style transfer algorithms, after traffic scene has been fully understood through training CNN. In this paper, style transfer is firstly introduced for generating thermal textures from color visible images. Siamese semantic loss for visible-infrared transformation is designed and introduced to generate object-oriented thermal infrared textures, while maintaining high definition. Compared to other style transfer algorithms, SS-CycleGAN could generate reasonable thermal infrared textures with clear edge details, in traffic scenes.