Learning A Physical-aware Diffusion Model Based on Transformer for Underwater Image Enhancement

Chen Zhao, Chenyu Dong, Weiling Cai
Computer Science, Computer Vision and Pattern Recognition, Computer Vision and Pattern Recognition (cs.CV)
2024-03-03 00:00:00
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.
PDF: Learning A Physical-aware Diffusion Model Based on Transformer for Underwater Image Enhancement.pdf
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