{"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-to-extract-a-video-sequence-from-a","title":"Learning to Extract a Video Sequence from a Single Motion-Blurred Image","arxiv_id":"1804.04065","date":"2018-04-11","proceeding":"CVPR 2018 6","authors":["Meiguang Jin","Givi Meishvili","Paolo Favaro"],"abstract":"We present a method to extract a video sequence from a single motion-blurred\nimage. Motion-blurred images are the result of an averaging process, where\ninstant frames are accumulated over time during the exposure of the sensor.\nUnfortunately, reversing this process is nontrivial. Firstly, averaging\ndestroys the temporal ordering of the frames. Secondly, the recovery of a\nsingle frame is a blind deconvolution task, which is highly ill-posed. We\npresent a deep learning scheme that gradually reconstructs a temporal ordering\nby sequentially extracting pairs of frames. Our main contribution is to\nintroduce loss functions invariant to the temporal order. This lets a neural\nnetwork choose during training what frame to output among the possible\ncombinations. We also address the ill-posedness of deblurring by designing a\nnetwork with a large receptive field and implemented via resampling to achieve\na higher computational efficiency. Our proposed method can successfully\nretrieve sharp image sequences from a single motion blurred image and can\ngeneralize well on synthetic and real datasets captured with different cameras.","url_abs":"http://arxiv.org/abs/1804.04065v1","url_pdf":"http://arxiv.org/pdf/1804.04065v1.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-to-extract-a-video-sequence-from-a","repo_url":"https://github.com/MeiguangJin/Learning-to-Extract-a-Video-Sequence-from-a-Single-Motion-Blurred-Image","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"deblurring","task_name":"Deblurring"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.04065","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}