SeaDiff: Underwater Image Enhancement with Degradation-Aware Diffusion Model

Light propagation in underwater scenes is significantly hindered by wavelength- and distance-dependent attenuation and scattering, leading to low contrast and severe color distortion in underwater images. Recent advancements in diffusion models have shown impressive performance in image restoration by learning data distribution prior knowledge (diffusion prior) from large amounts of paired data. However, due to the difficulties in collecting paired underwater images, the available data for underwater image enhancement is limited in both quality and quantity. This scarcity leads to a biased diffusion prior and suboptimal performance of diffusion models. To address this issue, we propose a novel method, termed SeaDiff, to learn underwater diffusion prior with wavelength- and distance-dependent degradation awareness. Specifically, we introduce a Prior Knowledge Mining Model (PKMM), which includes two key components: (1) the Physical Prior Embedding Module (PPEM) that simulates the underwater imaging process through a distance-dependent physical model and embeds physical prior by incorporating generalizable distance-aware cues from a large vision foundation model; and (2) the Color Prior Embedding Module (CPEM) that extracts wavelength-dependent color distribution prior from a log-chroma color space. Additionally, we propose a Degradation-Aware Diffusion Model (DADM) that seamlessly integrates degradation prior with diffusion prior and enhances the underwater images with high visual quality. Extensive experiments on popular UIE benchmarks and downstream tasks demonstrate that the proposed SeaDiff achieves state-of-the-art performance in terms of both visual quality and quantitative metrics. The code will be released at https://github.com/Henry-Bi/SeaDiff.