  Underwater image enhancement (UIE) is challenging since image degradation in
aquatic environments is complicated and changing over time. Existing mainstream
methods rely on either physical-model or data-driven, suffering from
performance bottlenecks due to changes in imaging conditions or training
instability. In this article, we make the first attempt to adapt the diffusion
model to the UIE task and propose a Content-Preserving Diffusion Model (CPDM)
to address the above challenges. CPDM first leverages a diffusion model as its
fundamental model for stable training and then designs a content-preserving
framework to deal with changes in imaging conditions. Specifically, we
construct a conditional input module by adopting both the raw image and the
difference between the raw and noisy images as the input, which can enhance the
model's adaptability by considering the changes involving the raw images in
underwater environments. To preserve the essential content of the raw images,
we construct a content compensation module for content-aware training by
extracting low-level features from the raw images. Extensive experimental
results validate the effectiveness of our CPDM, surpassing the state-of-the-art
methods in terms of both subjective and objective metrics.
