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.

翻译：水下图像增强（UIE）旨在从低质量水下图像中生成清晰图像。由于缺乏清晰的参考图像，研究者常通过合成方法构建配对数据集以训练深度模型。然而，这些合成图像有时存在质量缺陷，进而影响训练效果。为解决该问题，我们提出基于扩散先验的水下图像增强方法（UIEDP），该新型框架将UIE视为以退化水下输入为条件的清晰图像后验分布采样过程。具体而言，UIEDP将预训练的捕捉自然图像先验的扩散模型与任意现有UIE算法相结合，利用后者引导条件生成过程。扩散先验能够缓解低质量合成图像的缺陷，从而生成更高质量的图像。大量实验表明，UIEDP在多种评估指标上均取得显著提升，尤其在无参考图像质量评估方面表现突出，且生成的增强图像呈现出更自然的外观。