{"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/frame-consistent-recurrent-video-deraining","title":"Frame-Consistent Recurrent Video Deraining With Dual-Level Flow","arxiv_id":null,"date":"2019-06-01","proceeding":"CVPR 2019 6","authors":["Wenhan Yang"," Jiaying Liu"," Jiashi Feng"],"abstract":"In this paper, we address the problem of rain removal from videos by proposing a more comprehensive framework that considers the additional degradation factors in real scenes neglected in previous works. The proposed framework is built upon a two-stage recurrent network with dual-level flow regularizations to perform the inverse recovery process of the rain synthesis model for video deraining. The rain-free frame is estimated from the single rain frame at the first stage. It is then taken as guidance along with previously recovered clean frames to help obtain a more accurate clean frame at the second stage. This two-step architecture is capable of extracting more reliable motion information from the initially estimated rain-free frame at the first stage for better frame alignment and motion modeling at the second stage. Furthermore, to keep the motion consistency between frames that facilitates a frame-consistent deraining model at the second stage, a dual-level flow based regularization is proposed at both coarse flow and fine pixel levels. To better train and evaluate the proposed video deraining network, a novel rain synthesis model is developed to produce more visually authentic paired training and evaluation videos.  Extensive experiments on a series of synthetic and real videos verify not only the superiority of the proposed method over state-of-the-art but also the effectiveness of network design and its each component.\r","url_abs":"http://openaccess.thecvf.com/content_CVPR_2019/html/Yang_Frame-Consistent_Recurrent_Video_Deraining_With_Dual-Level_Flow_CVPR_2019_paper.html","url_pdf":"http://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_Frame-Consistent_Recurrent_Video_Deraining_With_Dual-Level_Flow_CVPR_2019_paper.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":"frame-consistent-recurrent-video-deraining","repo_url":"https://github.com/flyywh/Dual-FLow-Video-Deraining-CVPR-2019","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"rain-removal","task_name":"Rain Removal"},{"task_slug":"video-deraining","task_name":"Video deraining"}],"methods":[],"datasets_introduced":[{"slug":"nighrain","name":"Nightrain","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}