{"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/revisiting-temporal-alignment-for-video","title":"Revisiting Temporal Alignment for Video Restoration","arxiv_id":"2111.15288","date":"2021-11-30","proceeding":"CVPR 2022 1","authors":["Kun Zhou","Wenbo Li","Liying Lu","Xiaoguang Han","Jiangbo Lu"],"abstract":"Long-range temporal alignment is critical yet challenging for video restoration tasks. Recently, some works attempt to divide the long-range alignment into several sub-alignments and handle them progressively. Although this operation is helpful in modeling distant correspondences, error accumulation is inevitable due to the propagation mechanism. In this work, we present a novel, generic iterative alignment module which employs a gradual refinement scheme for sub-alignments, yielding more accurate motion compensation. To further enhance the alignment accuracy and temporal consistency, we develop a non-parametric re-weighting method, where the importance of each neighboring frame is adaptively evaluated in a spatial-wise way for aggregation. By virtue of the proposed strategies, our model achieves state-of-the-art performance on multiple benchmarks across a range of video restoration tasks including video super-resolution, denoising and deblurring. Our project is available in \\url{https://github.com/redrock303/Revisiting-Temporal-Alignment-for-Video-Restoration.git}.","url_abs":"https://arxiv.org/abs/2111.15288v2","url_pdf":"https://arxiv.org/pdf/2111.15288v2.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":"revisiting-temporal-alignment-for-video","repo_url":"https://github.com/redrock303/revisiting-temporal-alignment-for-video-restoration","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deblurring","task_name":"Deblurring"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"motion-compensation","task_name":"Motion Compensation"},{"task_slug":"super-resolution","task_name":"Super-Resolution"},{"task_slug":"video-restoration","task_name":"Video Restoration"},{"task_slug":"video-super-resolution","task_name":"Video Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-super-resolution-on-vimeo-90k","task":"Video Super-Resolution","dataset":"Vimeo-90K","model":"RTA-Vimeo-90K","rank_in_archive_order":1,"of":1,"metrics":{"Average PSNR":"37.84"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2111.15288","atlas_url":"https://app.syntology.ai/?focus=2111.15288","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.15288"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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