UIEDP:Underwater Image Enhancement with Diffusion Prior

TL;DR

UIEDP introduces a novel underwater image enhancement framework that combines diffusion models with existing algorithms to produce higher-quality, more natural underwater images without relying solely on synthetic references.

Contribution

The paper presents a new diffusion prior-based framework for underwater image enhancement that improves quality by mitigating synthetic data limitations.

Findings

Significant improvements in image quality metrics.

Enhanced natural appearance of underwater images.

Robust performance across various datasets.

Abstract

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.