With the rapid development of deep learning, video deraining has experienced significant progress. However, existing video deraining pipelines cannot achieve satisfying performance for scenes with rain layers of complex spatio-temporal distribution. In this paper, we approach video deraining by employing an event camera. As a neuromorphic sensor, the event camera suits scenes of non-uniform motion and dynamic light conditions. We propose an end-to-end learning-based network to unlock the potential of the event camera for video deraining. First, we devise an event-aware motion detection module to adaptively aggregate multi-frame motion contexts using event-aware masks. Second, we design a pyramidal adaptive selection module for reliably separating the background and rain layers by incorporating multi-modal contextualized priors. In addition, we build a real-world dataset consisting of rainy videos and temporally synchronized event streams. We compare our method with extensive state-of-the-art methods on synthetic and self-collected real-world datasets, demonstrating the clear superiority of our method. The code and dataset are available at \url{https://github.com/booker-max/EGVD}.

翻译：随着深度学习的快速发展，视频去雨技术取得了显著进步。然而，现有视频去雨方法在应对具有复杂时空分布的雨层场景时，仍难以获得令人满意的性能。本文采用事件相机进行视频去雨研究。作为一种神经形态传感器，事件相机适用于非均匀运动与动态光照条件场景。我们提出了一种基于端到端学习的网络，以挖掘事件相机在视频去雨中的潜力。首先，我们设计了事件感知运动检测模块，利用事件感知遮罩自适应地聚合多帧运动上下文信息。其次，构建了金字塔自适应选择模块，通过融合多模态上下文先验可靠分离背景与雨层。此外，我们构建了一个包含雨天视频与时序同步事件流的真实世界数据集。在合成数据集与自采真实数据集上的对比实验表明，本方法显著优于现有最优方法。代码与数据集已开源至\url{https://github.com/booker-max/EGVD}。