Underwater Image Enhancement (UIE) is essential for mitigating degradations caused by water medium. Although learning-based methods have advanced significantly, most rely on paired datasets with unstable label quality, which bottlenecks model performance. This paper proposes a diffusion-based, in-dataset self-supervised learning strategy designed to exploit the quality distribution of training labels. Specifically, we evaluate label quality via semantic perception embeddings from a pre-trained diffusion model in a training-free manner. These quality scores are subsequently quantized into noise-level indices, guiding a multi-step denoising process for level-wise supervision. This mechanism prevents low-quality labels from degrading the model while maximizing their utility during training. Furthermore, a Fourier-based refinement network is incorporated to explicitly reconstruct high-frequency components. Extensive evaluations demonstrate that our method consistently outperforms SOTA approaches in restoration quality. The code and pre-trained model will be available once accepted in link.

翻译：水下图像增强（UIE）对于减轻水介质造成的退化至关重要。尽管基于学习的方法已取得显著进展，但多数方法依赖于标签质量不稳定的配对数据集，这成为模型性能的瓶颈。本文提出一种基于扩散的数据集内自监督学习策略，旨在充分利用训练标签的质量分布。具体而言，我们通过预训练扩散模型中的语义感知嵌入，以无需训练的方式评估标签质量。这些质量分数随后被量化为噪声等级索引，用于指导多步去噪过程中的等级监督。该机制可防止低质量标签降低模型性能，同时最大化其在训练过程中的效用。此外，我们引入基于傅里叶变换的精炼网络以显式重建高频分量。大量评估表明，我们的方法在恢复质量上持续优于现有最优方法。代码与预训练模型将在论文被接收后通过链接公开。