Learning A Physical-aware Diffusion Model Based on Transformer for
  Underwater Image Enhancement
Chen Zhao Chenyu Dong Weiling Cai
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 and Physics-aware
Diffusion Transformer (PDT) Branch. Our designed PPG branch is a plug-and-play
network to produce the physics prior, which can be integrated into any deep
framework. 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. Extensive experiments prove that
our method achieves best performance on UIE tasks.