Abstract
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 this https URL.
Abstract (translated)
在低光条件下，无人驾驶航空器（UAV）的性能会显著下降，特别是在关键应用中。现有的低光图像增强（LIE）方法难以应对航拍图像特有的挑战，包括超高分辨率（UHR）、缺乏配对数据、严重的非均匀照明以及部署限制等问题。为了应对这些问题，我们提出了三项主要贡献： 第一项是推出了U3D，这是首个用于LIE的无监督超高分辨率无人机数据集，并附带统一评估工具包。 第二项是引入了边缘效率指数（EEI），这是一种新颖的指标，在感知质量与关键部署因素（速度、分辨率、模型复杂度和内存占用）之间实现了平衡。 第三项是我们开发了一个高效的框架U3LIE，该框架包含两项训练专用设计：自适应预增强数据扩充（APA），用于输入标准化；以及亮度区间损失（L_int），用于曝光控制。U3LIE在处理4K图像方面达到了最先进的水平，在单个GPU上可以实现每秒23.8帧的速度，使其非常适合实时机载部署。 综上所述，这些贡献提供了一个全面的解决方案（数据集、指标和方法）来推进全天候无人机视觉技术的发展。代码和数据集可以在提供的链接中获取。
URL
https://arxiv.org/abs/2509.01373
