Underwater visuals undergo various complex degradations, inevitably influencing the efficiency of underwater vision tasks. Recently, diffusion models were employed to underwater image enhancement (UIE) tasks, and gained SOTA performance. However, these methods fail to consider the physical properties and underwater imaging mechanisms in the diffusion process, limiting information completion capacity of diffusion models. In this paper, we introduce a novel UIE framework, named PA-Diff, designed to exploiting the knowledge of physics to guide the diffusion process. PA-Diff consists of Physics Prior Generation (PPG) Branch, Implicit Neural Reconstruction (INR) Branch, and Physics-aware Diffusion Transformer (PDT) Branch. Our designed PPG branch aims to produce the prior knowledge of physics. With utilizing the physics prior knowledge to guide the diffusion process, PDT branch can obtain underwater-aware ability and model the complex distribution in real-world underwater scenes. INR Branch can learn robust feature representations from diverse underwater image via implicit neural representation, which reduces the difficulty of restoration for PDT branch. Extensive experiments prove that our method achieves best performance on UIE tasks.

翻译：水下视觉图像经历多种复杂退化，不可避免地影响水下视觉任务的效率。近期，扩散模型被应用于水下图像增强任务，并取得了最先进的性能。然而，这些方法在扩散过程中未能考虑物理特性与水下成像机制，限制了扩散模型的信息补全能力。本文提出一种新颖的水下图像增强框架PA-Diff，旨在利用物理知识引导扩散过程。PA-Diff由物理先验生成分支、隐式神经重建分支和物理感知扩散Transformer分支构成。我们设计的物理先验生成分支旨在生成物理先验知识。通过利用物理先验知识引导扩散过程，物理感知扩散Transformer分支能够获得水下感知能力，并对真实水下场景中的复杂分布进行建模。隐式神经重建分支可通过隐式神经表示从多样化水下图像中学习鲁棒的特征表示，从而降低物理感知扩散Transformer分支的复原难度。大量实验证明，本方法在水下图像增强任务中取得了最佳性能。