RQUL-UIE: Revitalizing Quality-Unstable Labels for Underwater Image Enhancement via In-Dataset Self-Supervision

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On September 7, 2026, arXiv cs.CV published the paper RQUL-UIE, aiming to solve the problem of unstable labels in underwater image enhancement through an in-dataset self-supervised mechanism. The study proposed a new method that utilizes in-dataset self-supervised learning to revitalize these unstable labels, thereby improving the quality of underwater image enhancement.

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RQUL-UIE

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RQUL-UIE1

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A arXiv cs.CV en 2026-09-07 12:00

本文提出一种基于扩散模型的在数据集自监督学习策略，旨在利用训练标签的质量分布提升水下图像增强性能。该方法通过预训练扩散模型以无训练方式评估标签质量，并将评分量化为噪声水平索引以指导分层去噪过程，防止低质标签干扰模型同时最大化其效用；此外引入傅里叶基细化网络重构高频分量。实验表明，该策略在恢复质量上持续优于最先进方法。代码与预训练模型将在论文被接收后通过链接提供。