UIEDP:Underwater Image Enhancement with Diffusion Prior
Dazhao Du Enhan Li Lingyu Si Fanjiang Xu Jianwei Niu Fuchun Sun
Underwater image enhancement (UIE) aims to generate clear images from
low-quality underwater images. Due to the unavailability of clear reference
images, researchers often synthesize them to construct paired datasets for
training deep models. However, these synthesized images may sometimes lack
quality, adversely affecting training outcomes. To address this issue, we
propose UIE with Diffusion Prior (UIEDP), a novel framework treating UIE as a
posterior distribution sampling process of clear images conditioned on degraded
underwater inputs. Specifically, UIEDP combines a pre-trained diffusion model
capturing natural image priors with any existing UIE algorithm, leveraging the
latter to guide conditional generation. The diffusion prior mitigates the
drawbacks of inferior synthetic images, resulting in higher-quality image
generation. Extensive experiments have demonstrated that our UIEDP yields
significant improvements across various metrics, especially no-reference image
quality assessment. And the generated enhanced images also exhibit a more
natural appearance.