PID: Physics-Informed Diffusion Model for Infrared Image Generation
<|reference_start|>PID: Physics-Informed Diffusion Model for Infrared Image Generation: 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.<|reference_end|>
@article{mao2024pid:,
    title={PID: Physics-Informed Diffusion Model for Infrared Image Generation},
    author={Fangyuan Mao, Jilin Mei, Shun Lu, Fuyang Liu, Liang Chen, Fangzhou
  Zhao, Yu Hu},
    journal={arXiv preprint arXiv:2407.09299},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09299},
    primaryClass={cs.CV}
}