{"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/towards-interpretable-video-super-resolution","title":"Towards Interpretable Video Super-Resolution via Alternating Optimization","arxiv_id":"2207.10765","date":"2022-07-21","proceeding":null,"authors":["JieZhang Cao","Jingyun Liang","Kai Zhang","Wenguan Wang","Qin Wang","Yulun Zhang","Hao Tang","Luc van Gool"],"abstract":"In this paper, we study a practical space-time video super-resolution (STVSR) problem which aims at generating a high-framerate high-resolution sharp video from a low-framerate low-resolution blurry video. Such problem often occurs when recording a fast dynamic event with a low-framerate and low-resolution camera, and the captured video would suffer from three typical issues: i) motion blur occurs due to object/camera motions during exposure time; ii) motion aliasing is unavoidable when the event temporal frequency exceeds the Nyquist limit of temporal sampling; iii) high-frequency details are lost because of the low spatial sampling rate. These issues can be alleviated by a cascade of three separate sub-tasks, including video deblurring, frame interpolation, and super-resolution, which, however, would fail to capture the spatial and temporal correlations among video sequences. To address this, we propose an interpretable STVSR framework by leveraging both model-based and learning-based methods. Specifically, we formulate STVSR as a joint video deblurring, frame interpolation, and super-resolution problem, and solve it as two sub-problems in an alternate way. For the first sub-problem, we derive an interpretable analytical solution and use it as a Fourier data transform layer. Then, we propose a recurrent video enhancement layer for the second sub-problem to further recover high-frequency details. Extensive experiments demonstrate the superiority of our method in terms of quantitative metrics and visual quality.","url_abs":"https://arxiv.org/abs/2207.10765v1","url_pdf":"https://arxiv.org/pdf/2207.10765v1.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":"towards-interpretable-video-super-resolution","repo_url":"https://github.com/caojiezhang/davsr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"deblurring","task_name":"Deblurring"},{"task_slug":"space-time-video-super-resolution","task_name":"Space-time Video Super-resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"},{"task_slug":"video-deblurring","task_name":"Video Deblurring"},{"task_slug":"video-enhancement","task_name":"Video Enhancement"},{"task_slug":"video-super-resolution","task_name":"Video Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-super-resolution-on-reds4-4x-upscaling","task":"Video Super-Resolution","dataset":"REDS4- 4x upscaling","model":"DAVSR","rank_in_archive_order":7,"of":7,"metrics":{"PSNR":"29.12","SSIM":"0.859"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2207.10765","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}