  Infrared imaging technology has gained significant attention for its reliable
sensing ability in low visibility conditions, prompting many studies to convert
the abundant RGB images to infrared images. However, most existing image
translation methods treat infrared images as a stylistic variation, neglecting
the underlying physical laws, which limits their practical application. To
address these issues, we propose a Physics-Informed Diffusion (PID) model for
translating RGB images to infrared images that adhere to physical laws. Our
method leverages the iterative optimization of the diffusion model and
incorporates strong physical constraints based on prior knowledge of infrared
laws during training. This approach enhances the similarity between translated
infrared images and the real infrared domain without increasing extra training
parameters. Experimental results demonstrate that PID significantly outperforms
existing state-of-the-art methods. Our code is available at
https://github.com/fangyuanmao/PID.
