Learning a Physical-Aware Diffusion Model Based on Transformer for Underwater Image Enhancement

Underwater visuals undergo various complex degra-dations, inevitably influencing the efficiency of underwater vision tasks. Recently, diffusion models were employed to underwater image enhancement (UIE) tasks, and gained the best 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 exploit physical knowledge to guide the diffusion process. PA-Diff consists of the Physics Prior Generation (PPG) branch, the Implicit Neural Reconstruction (INR) branch, and the 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 images via implicit neural representation, which reduces the difficulty of restoration for PDT branch. Extensive experiments demonstrate that our method achieves the best performance on UIE tasks. The code is available at https://github.com/chenydong/PA-Diff.