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.

翻译：红外成像技术因其在低能见度条件下可靠的感知能力而受到广泛关注，促使许多研究将丰富的RGB图像转换为红外图像。然而，现有的大多数图像转换方法将红外图像视为一种风格变化，忽略了其背后的物理规律，这限制了它们的实际应用。为解决这些问题，我们提出了一种物理信息扩散（PID）模型，用于将RGB图像转换为符合物理规律的红外图像。我们的方法利用扩散模型的迭代优化，并在训练过程中基于红外规律的先验知识引入强物理约束。这一方法在不增加额外训练参数的情况下，提升了转换后的红外图像与真实红外域之间的相似性。实验结果表明，PID显著优于现有的先进方法。我们的代码可在https://github.com/fangyuanmao/PID获取。