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In\ncontrast to most prior work where frames are pooled together by stacking or\nwarping, our model, the Recurrent Back-Projection Network (RBPN) treats each\ncontext frame as a separate source of information. These sources are combined\nin an iterative refinement framework inspired by the idea of back-projection in\nmultiple-image super-resolution. This is aided by explicitly representing\nestimated inter-frame motion with respect to the target, rather than explicitly\naligning frames. We propose a new video super-resolution benchmark, allowing\nevaluation at a larger scale and considering videos in different motion\nregimes. Experimental results demonstrate that our RBPN is superior to existing\nmethods on several datasets.","url_abs":"http://arxiv.org/abs/1903.10128v1","url_pdf":"http://arxiv.org/pdf/1903.10128v1.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":"recurrent-back-projection-network-for-video","repo_url":"https://github.com/zhangtianmingxp/rbpn_mindspore","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mindspore","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"recurrent-back-projection-network-for-video","repo_url":"https://github.com/2023-MindSpore-1/ms-code-216/tree/main/rbpn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"recurrent-back-projection-network-for-video","repo_url":"https://github.com/Mind23-2/MindCode-5/tree/main/rbpn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"recurrent-back-projection-network-for-video","repo_url":"https://github.com/MindSpore-paper-code-2/code2/tree/main/rbpn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"recurrent-back-projection-network-for-video","repo_url":"https://github.com/alterzero/RBPN-PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"recurrent-back-projection-network-for-video","repo_url":"https://github.com/code-implementation1/Code6/tree/main/ras","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"recurrent-back-projection-network-for-video","repo_url":"https://github.com/xiuyu0000/new_papers_codes/tree/main/rbpn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"},{"task_slug":"video-super-resolution","task_name":"Video Super-Resolution"}],"methods":[{"method_slug":"rbpn","method_name":"RBPN"}],"datasets_introduced":[],"methods_introduced":[{"slug":"rbpn","name":"RBPN","full_name":"Recurrent Back Projection Network"}],"results":[{"leaderboard":"/sota/video-super-resolution-on-msu-super-1","task":"Video Super-Resolution","dataset":"MSU Super-Resolution for Video Compression","model":"RBPN + x264","rank_in_archive_order":25,"of":85,"metrics":{"BSQ-rate over ERQA":"1.599","BSQ-rate over LPIPS":"1.335","BSQ-rate over MS-SSIM":"0.729","BSQ-rate over PSNR":"1.127","BSQ-rate over Subjective Score":"1.498","BSQ-rate over VMAF":"0.733"},"uses_additional_data":false},{"leaderboard":"/sota/video-super-resolution-on-msu-super-1","task":"Video Super-Resolution","dataset":"MSU Super-Resolution for Video Compression","model":"RBPN + x265","rank_in_archive_order":32,"of":85,"metrics":{"BSQ-rate over ERQA":"13.185","BSQ-rate over LPIPS":"13.237","BSQ-rate over MS-SSIM":"1.438","BSQ-rate over PSNR":"1.89","BSQ-rate over Subjective Score":"2.282","BSQ-rate over VMAF":"1.324"},"uses_additional_data":false},{"leaderboard":"/sota/video-super-resolution-on-msu-super-1","task":"Video Super-Resolution","dataset":"MSU Super-Resolution for Video Compression","model":"RBPN + aomenc","rank_in_archive_order":37,"of":85,"metrics":{"BSQ-rate over ERQA":"13.572","BSQ-rate over LPIPS":"5.821","BSQ-rate over MS-SSIM":"3.089","BSQ-rate over PSNR":"10.89","BSQ-rate over Subjective Score":"2.7","BSQ-rate over VMAF":"1.996"},"uses_additional_data":false},{"leaderboard":"/sota/video-super-resolution-on-msu-super-1","task":"Video Super-Resolution","dataset":"MSU Super-Resolution for Video Compression","model":"RBPN + vvenc","rank_in_archive_order":38,"of":85,"metrics":{"BSQ-rate over ERQA":"18.314","BSQ-rate over LPIPS":"11.777","BSQ-rate over MS-SSIM":"0.884","BSQ-rate over PSNR":"5.783","BSQ-rate over Subjective Score":"2.719","BSQ-rate over VMAF":"0.689"},"uses_additional_data":false},{"leaderboard":"/sota/video-super-resolution-on-msu-super-1","task":"Video Super-Resolution","dataset":"MSU Super-Resolution for Video Compression","model":"RBPN + uavs3e","rank_in_archive_order":43,"of":85,"metrics":{"BSQ-rate over ERQA":"7.133","BSQ-rate over LPIPS":"4.859","BSQ-rate over MS-SSIM":"2.263","BSQ-rate over PSNR":"6.301","BSQ-rate over Subjective Score":"2.944","BSQ-rate over VMAF":"0.702"},"uses_additional_data":false},{"leaderboard":"/sota/video-super-resolution-on-msu-vsr-benchmark","task":"Video Super-Resolution","dataset":"MSU Video Super Resolution Benchmark: Detail Restoration","model":"RBPN","rank_in_archive_order":3,"of":32,"metrics":{"1 - 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