Raindrops adhering to the lens of UAVs can obstruct visibility of the background scene and degrade image quality. Despite recent progress in image deraining methods and datasets, there is a lack of focus on raindrop removal from UAV aerial imagery due to the unique challenges posed by varying angles and rapid movement during drone flight. To fill the gap in this research, we first construct a new benchmark dataset for removing raindrops from UAV images, called UAV-Rain1k. In this letter, we provide a dataset generation pipeline, which includes modeling raindrop shapes using Blender, collecting background images from various UAV angles, random sampling of rain masks and etc. Based on the proposed benchmark, we further present a comprehensive evaluation of existing representative image deraining algorithms, and reveal future research opportunities worth exploring. The proposed dataset will be publicly available at https://github.com/cschenxiang/UAV-Rain1k.

翻译：无人机镜头上的雨滴会遮挡背景场景的可见性，降低图像质量。尽管近年图像去雨方法和数据集取得了进展，但由于无人机飞行过程中角度变化和快速运动带来的独特挑战，针对无人机航拍图像的雨滴去除研究仍缺乏关注。为填补这一研究空白，我们首先构建了一个新的无人机图像雨滴去除基准数据集UAV-Rain1k。本报告中，我们提出了数据集生成流程，包括使用Blender建模雨滴形状、从不同无人机角度收集背景图像、随机采样雨滴掩膜等。基于该基准数据集，我们进一步对现有代表性图像去雨算法进行了全面评估，并揭示了值得探索的未来研究方向。该数据集将在https://github.com/cschenxiang/UAV-Rain1k公开提供。