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 this https URL

使用 Blender 模拟雨滴形状，收集不同无人机角度的背景图像，随机采样雨滴遮罩等构建了一个新的用于去除无人机图像中雨滴的基准数据集 UAV-Rain1k，并在此基准数据集上全面评估了现有的代表性图像去雨算法，并公布了值得进一步研究的未来研究机会。