研究人员开发了一种名为RQUL-UIE的新方法，通过解决训练数据中不稳定的标签质量问题来改进水下图像增强。该方法采用基于扩散的自监督学习策略，利用预训练扩散模型的语义感知嵌入来评估标签质量。这使得系统能够有效利用低质量标签，同时防止其降低性能，并且基于傅里叶的网络进一步优化高频分量以获得卓越的恢复效果。 AI
影响 改进图像恢复技术，可能使海洋研究和水下探索等应用受益。
排序理由 该集群包含一篇详细介绍新颖图像增强方法的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2606.06176v1 Announce Type: new Abstract: 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 bottlen…
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 di…