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。

UAV-Rain1k：无人机航拍图像雨滴去除基准

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

Existing methods for single images raindrop removal either have poor
robustness or suffer from parameter burdens. In this paper, we propose a new
Adjacent Aggregation Network (A^2Net) with lightweight architectures to remove
raindrops from single images. Instead of directly cascading convolutional
layers, we design an adjacent aggregation architecture to better fuse features
for rich representations generation, which can lead to high quality images
reconstruction. To further simplify the learning process, we utilize a
problem-specific knowledge to force the network focus on the luminance channel
in the YUV color space instead of all RGB channels. By combining adjacent
aggregating operation with color space transformation, the proposed A^2Net can
achieve state-of-the-art performances on raindrop removal with significant
parameters reduction.

本研究提出使用轻量级架构的邻接聚合网络（A^2Net）在 YUV 颜色空间中聚合 RGB 通道，提高表征生成和高质量图像重建能力，从而实现降雨去除的最先进表现并显著减少参数。

A^2Net: 相邻聚合网络用于图像雨滴去除

A^2Net: Adjacent Aggregation Networks for Image Raindrop Removal

Raindrops adhered to a glass window or camera lens can severely hamper the
visibility of a background scene and degrade an image considerably. In this
paper, we address the problem by visually removing raindrops, and thus
transforming a raindrop degraded image into a clean one. The problem is
intractable, since first the regions occluded by raindrops are not given.
Second, the information about the background scene of the occluded regions is
completely lost for most part. To resolve the problem, we apply an attentive
generative network using adversarial training. Our main idea is to inject
visual attention into both the generative and discriminative networks. During
the training, our visual attention learns about raindrop regions and their
surroundings. Hence, by injecting this information, the generative network will
pay more attention to the raindrop regions and the surrounding structures, and
the discriminative network will be able to assess the local consistency of the
restored regions. This injection of visual attention to both generative and
discriminative networks is the main contribution of this paper. Our experiments
show the effectiveness of our approach, which outperforms the state of the art
methods quantitatively and qualitatively.

本文提出了一种基于生成对抗网络和视觉注意力机制的雨滴去除方法，成功地将雨滴污染的图像还原为干净图像，并取得了比同类方法更好的定量和定性效果。