针对水下非均匀光照，提出UNIR-Net与专用数据集，提升图像恢复效果。
UNIR-Net: A Novel Approach for Restoring Underwater Images with Non-Uniform Illumination Using Synthetic Data
- 融合光照增强、注意力机制等模块，协同修复复杂光照下的水下图像
- 在自建PUNI和真实NUID数据集上，定量指标与视觉质量均优于现有方法
- 适用于水下视觉任务，如语义分割，代码已开源
恢复受非均匀光照影响的水下图像对提升海洋应用中的视觉质量与可用性至关重要。传统方法难以应对复杂的光照模式，而基于学习的方法受限于缺乏针对性数据集。为此，本文提出水下非均匀光照恢复网络（UNIR-Net），集成光照增强、注意力机制、视觉精修与对比度校正等模块，有效恢复受非均匀光照影响的水下图像。同时，构建了专用于训练与评估的成对水下非均匀光照数据集（PUNI）。在PUNI与大规模真实数据集NUID上的实验表明，UNIR-Net在定量指标与视觉效果上均表现优异，并显著提升水下语义分割等下游任务性能。相关代码已公开于https://github.com/xingyumex/UNIR-Net。
原文摘要 · Abstract (English)
Restoring underwater images affected by non-uniform illumination (NUI) is essential to improve visual quality and usability in marine applications. Conventional methods often fall short in handling complex illumination patterns, while learning-based approaches face challenges due to the lack of targeted datasets. To address these limitations, the Underwater Non-uniform Illumination Restoration Network (UNIR-Net) is proposed. UNIR-Net integrates multiple components, including illumination enhancement, attention mechanisms, visual refinement, and contrast correction, to effectively restore underwater images affected by NUI. In addition, the Paired Underwater Non-uniform Illumination (PUNI) dataset is introduced, specifically designed for training and evaluating models under NUI conditions. Experimental results on PUNI and the large-scale real-world Non-Uniform Illumination Dataset (NUID) show that UNIR-Net achieves superior performance in both quantitative metrics and visual outcomes. UNIR-Net also improves downstream tasks such as underwater semantic segmentation, highlighting its practical relevance. The code of this method is available at https://github.com/xingyumex/UNIR-Net
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