EGVD: Event-Guided Video Deraining

Recent research has explored leveraging event cameras, known for their prowess in capturing scenes with nonuniform motion, for video deraining, leading to performance improvements. However, the existing event-based method still faces the challenge that the complex spatiotemporal distribution disrupts temporal information fusion and complicates feature separation. This article proposes a novel end-to-end learning framework for video deraining that effectively extracts the rich dynamic information provided by the event stream. Our framework incorporates two key modules: an event-aware motion detection (EAMD) module that adaptively aggregates multiframe motion information using event-driven masks and a pyramidal adaptive selection module that separates background and rain layers by leveraging contextual priors from both event and conventional camera data. To facilitate efficient training, we introduce a real-world dataset of synchronized rainy videos and event streams. Extensive evaluations on both synthetic and real-world datasets demonstrate the superiority of our proposed method compared to state-of-the-art approaches. The code is available at https://github.com/booker-max/EGVD.