UAV-Rain1k: A Benchmark for Raindrop Removal from UAV Aerial Imagery
This work addresses the lack of focus on raindrop removal for UAV imagery, which is crucial for improving visibility in drone applications, but it is incremental as it primarily provides a new dataset.
The authors tackled the problem of raindrop removal from UAV aerial imagery by constructing a new benchmark dataset called UAV-Rain1k, which includes a dataset generation pipeline and an evaluation of existing deraining algorithms to highlight research opportunities.
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 is publicly available at https://github.com/cschenxiang/UAV-Rain1k.