{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/egvd-event-guided-video-deraining","title":"EGVD: Event-Guided Video Deraining","arxiv_id":"2309.17239","date":"2023-09-29","proceeding":null,"authors":["Yueyi Zhang","Jin Wang","Wenming Weng","Xiaoyan Sun","Zhiwei Xiong"],"abstract":"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}.","url_abs":"https://arxiv.org/abs/2309.17239v1","url_pdf":"https://arxiv.org/pdf/2309.17239v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"egvd-event-guided-video-deraining","repo_url":"https://github.com/booker-max/egvd","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"motion-detection","task_name":"Motion Detection"},{"task_slug":"rain-removal","task_name":"Rain Removal"},{"task_slug":"video-deraining","task_name":"Video deraining"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}