{"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/learning-for-video-super-resolution-through","title":"Learning for Video Super-Resolution through HR Optical Flow Estimation","arxiv_id":"1809.08573","date":"2018-09-23","proceeding":null,"authors":["Longguang Wang","Yulan Guo","Zaiping Lin","Xinpu Deng","Wei An"],"abstract":"Video super-resolution (SR) aims to generate a sequence of high-resolution\n(HR) frames with plausible and temporally consistent details from their\nlow-resolution (LR) counterparts. The generation of accurate correspondence\nplays a significant role in video SR. It is demonstrated by traditional video\nSR methods that simultaneous SR of both images and optical flows can provide\naccurate correspondences and better SR results. However, LR optical flows are\nused in existing deep learning based methods for correspondence generation. In\nthis paper, we propose an end-to-end trainable video SR framework to\nsuper-resolve both images and optical flows. Specifically, we first propose an\noptical flow reconstruction network (OFRnet) to infer HR optical flows in a\ncoarse-to-fine manner. Then, motion compensation is performed according to the\nHR optical flows. Finally, compensated LR inputs are fed to a super-resolution\nnetwork (SRnet) to generate the SR results. Extensive experiments demonstrate\nthat HR optical flows provide more accurate correspondences than their LR\ncounterparts and improve both accuracy and consistency performance. Comparative\nresults on the Vid4 and DAVIS-10 datasets show that our framework achieves the\nstate-of-the-art performance.","url_abs":"http://arxiv.org/abs/1809.08573v2","url_pdf":"http://arxiv.org/pdf/1809.08573v2.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":"learning-for-video-super-resolution-through","repo_url":"https://github.com/LongguangWang/SOF-VSR-Super-Resolving-Optical-Flow-for-Video-Super-Resolution-","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"learning-for-video-super-resolution-through","repo_url":"https://github.com/LongguangWang/SOF-VSR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"motion-compensation","task_name":"Motion Compensation"},{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"},{"task_slug":"super-resolution","task_name":"Super-Resolution"},{"task_slug":"video-super-resolution","task_name":"Video Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-super-resolution-on-vid4-4x-upscaling","task":"Video Super-Resolution","dataset":"Vid4 - 4x upscaling","model":"SOF-VSR","rank_in_archive_order":18,"of":27,"metrics":{"MOVIE":"4.32","PSNR":"26.01","SSIM":"0.771"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.08573","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}