{"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/multiframe-motion-coupling-for-video-super","title":"Multiframe Motion Coupling for Video Super Resolution","arxiv_id":"1611.07767","date":"2016-11-23","proceeding":null,"authors":["Jonas Geiping","Hendrik Dirks","Daniel Cremers","Michael Moeller"],"abstract":"The idea of video super resolution is to use different view points of a\nsingle scene to enhance the overall resolution and quality. Classical energy\nminimization approaches first establish a correspondence of the current frame\nto all its neighbors in some radius and then use this temporal information for\nenhancement. In this paper, we propose the first variational super resolution\napproach that computes several super resolved frames in one batch optimization\nprocedure by incorporating motion information between the high-resolution image\nframes themselves. As a consequence, the number of motion estimation problems\ngrows linearly in the number of frames, opposed to a quadratic growth of\nclassical methods and temporal consistency is enforced naturally. We use\ninfimal convolution regularization as well as an automatic parameter balancing\nscheme to automatically determine the reliability of the motion information and\nreweight the regularization locally. We demonstrate that our approach yields\nstate-of-the-art results and even is competitive with machine learning\napproaches.","url_abs":"http://arxiv.org/abs/1611.07767v2","url_pdf":"http://arxiv.org/pdf/1611.07767v2.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":"multiframe-motion-coupling-for-video-super","repo_url":"https://github.com/HendrikMuenster/superResolution","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"motion-estimation","task_name":"Motion Estimation"},{"task_slug":"super-resolution","task_name":"Super-Resolution"},{"task_slug":"video-super-resolution","task_name":"Video Super-Resolution"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}