{"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/deepbinarymask-learning-a-binary-mask-for","title":"DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensing","arxiv_id":"1607.03343","date":"2016-07-12","proceeding":null,"authors":["Michael Iliadis","Leonidas Spinoulas","Aggelos K. Katsaggelos"],"abstract":"In this paper, we propose a novel encoder-decoder neural network model\nreferred to as DeepBinaryMask for video compressive sensing. In video\ncompressive sensing one frame is acquired using a set of coded masks (sensing\nmatrix) from which a number of video frames is reconstructed, equal to the\nnumber of coded masks. The proposed framework is an end-to-end model where the\nsensing matrix is trained along with the video reconstruction. The encoder\nlearns the binary elements of the sensing matrix and the decoder is trained to\nrecover the unknown video sequence. The reconstruction performance is found to\nimprove when using the trained sensing mask from the network as compared to\nother mask designs such as random, across a wide variety of compressive sensing\nreconstruction algorithms. Finally, our analysis and discussion offers insights\ninto understanding the characteristics of the trained mask designs that lead to\nthe improved reconstruction quality.","url_abs":"http://arxiv.org/abs/1607.03343v2","url_pdf":"http://arxiv.org/pdf/1607.03343v2.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":"deepbinarymask-learning-a-binary-mask-for","repo_url":"https://github.com/miliadis/DeepVideoCS","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-2-Clause"}}],"tasks":[{"task_slug":"compressive-sensing","task_name":"Compressive Sensing"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"video-compressive-sensing","task_name":"Video Compressive Sensing"},{"task_slug":"video-reconstruction","task_name":"Video Reconstruction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1607.03343","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1607.03343"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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