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

TL;DR

This paper introduces UAV-Rain1k, a new benchmark dataset for raindrop removal from UAV aerial images, addressing the unique challenges of drone imagery and evaluating existing deraining algorithms.

Contribution

The paper presents a novel UAV-Rain1k dataset specifically designed for raindrop removal in UAV images and provides a comprehensive evaluation of current deraining methods on this dataset.

Findings

Existing deraining algorithms show varied performance on UAV images.

The UAV-Rain1k dataset highlights the challenges of raindrop removal in UAV scenarios.

Future research directions are identified based on benchmark evaluations.

Abstract

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.