首个无监督超高清无人机低光图像增强方案，兼顾效果与实时部署。
Unsupervised Ultra-High-Resolution UAV Low-Light Image Enhancement: A Benchmark, Metric and Framework
- 提出无监督的超高清无人机图像数据集U3D，支持统一评估。
- 设计新指标EEI，平衡画质与速度、内存等部署需求。
- 框架U3LIE实现4K图像23.8帧/秒实时处理，适合机载部署。
低光照严重削弱无人机在关键任务中的表现。现有低光图像增强方法难以应对航拍图像特有的挑战：超高清分辨率、缺乏成对数据、严重非均匀光照及部署限制。为此，本文提出三项核心贡献：首先，构建首个无监督的超高清无人机图像数据集U3D，并配套统一评估工具包；其次，提出边缘效率指数（EEI），综合衡量感知质量与推理速度、分辨率、模型复杂度和内存开销；最后，提出U3LIE高效框架，采用两项仅训练阶段设计——自适应预增强增广（APA）实现输入归一化，亮度区间损失（L_int）控制曝光。U3LIE实现当前最优性能，在单张GPU上处理4K图像达23.8 FPS，适用于实时机载部署。上述工作构成从数据、评估到方法的完整解决方案，推动全天候无人机视觉发展。代码与数据集见https://github.com/lwCVer/U3D_Toolkit。
原文摘要 · Abstract (English)
Low light conditions significantly degrade Unmanned Aerial Vehicles (UAVs) performance in critical applications. Existing Low-light Image Enhancement (LIE) methods struggle with the unique challenges of aerial imagery, including Ultra-High Resolution (UHR), lack of paired data, severe non-uniform illumination, and deployment constraints. To address these issues, we propose three key contributions. First, we present U3D, the first unsupervised UHR UAV dataset for LIE, with a unified evaluation toolkit. Second, we introduce the Edge Efficiency Index (EEI), a novel metric balancing perceptual quality with key deployment factors: speed, resolution, model complexity, and memory footprint. Third, we develop U3LIE, an efficient framework with two training-only designs-Adaptive Pre-enhancement Augmentation (APA) for input normalization and a Luminance Interval Loss (L_int) for exposure control. U3LIE achieves SOTA results, processing 4K images at 23.8 FPS on a single GPU, making it ideal for real-time on-board deployment. In summary, these contributions provide a holistic solution (dataset, metric, and method) for advancing robust 24/7 UAV vision. The code and datasets are available at https://github.com/lwCVer/U3D_Toolkit.
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