UAV-Rain1k: A Benchmark for Raindrop Removal from UAV Aerial Imagery

Wenhui Chang, Hongming Chen, Xin He, Xiang Chen, Liangduo Shen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 15-22

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 paper 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 is publicly available at https://github.com/cschenxiang/UAV-Rain1k.