Deep Feature Prior-Guided Conditional Diffusion Model for Underwater Image Enhancement

Underwater imaging always suffers from color distortion and reduced visibility due to light absorption and scattering, severely hindering visual perception and analysis. In this letter, we propose an underwater image enhancement framework based on diffusion model augmented with two lightweight guidance modules. The first module is a conditional branch that extracts structural features from a coarsely enhanced version to guide the denoising process toward more faithful restoration. While the second module retrieves high-quality features from a pre-constructed feature dictionary as priors, effectively restoring colors and fine details in degraded regions. Extensive experiments on public underwater image datasets demonstrate that our proposed method outperforms the state-of-the-art approaches both quantitatively and visually. It also generalizes well across various underwater environments, highlighting the effectiveness of incorporating structural and feature-level guidance into the diffusion process. The source code and pre-trained model are available at https://github.com/Juneit/PGUIE.