{"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/rank-minimization-for-snapshot-compressive","title":"Rank Minimization for Snapshot Compressive Imaging","arxiv_id":"1807.07837","date":"2018-07-20","proceeding":null,"authors":["Yang Liu","Xin Yuan","Jinli Suo","David J. Brady","Qionghai Dai"],"abstract":"Snapshot compressive imaging (SCI) refers to compressive imaging systems\nwhere multiple frames are mapped into a single measurement, with video\ncompressive imaging and hyperspectral compressive imaging as two representative\napplications. Though exciting results of high-speed videos and hyperspectral\nimages have been demonstrated, the poor reconstruction quality precludes SCI\nfrom wide applications.This paper aims to boost the reconstruction quality of\nSCI via exploiting the high-dimensional structure in the desired signal. We\nbuild a joint model to integrate the nonlocal self-similarity of\nvideo/hyperspectral frames and the rank minimization approach with the SCI\nsensing process. Following this, an alternating minimization algorithm is\ndeveloped to solve this non-convex problem. We further investigate the special\nstructure of the sampling process in SCI to tackle the computational workload\nand memory issues in SCI reconstruction. Both simulation and real data\n(captured by four different SCI cameras) results demonstrate that our proposed\nalgorithm leads to significant improvements compared with current\nstate-of-the-art algorithms. We hope our results will encourage the researchers\nand engineers to pursue further in compressive imaging for real applications.","url_abs":"http://arxiv.org/abs/1807.07837v1","url_pdf":"http://arxiv.org/pdf/1807.07837v1.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":"rank-minimization-for-snapshot-compressive","repo_url":"https://github.com/liuyang12/DeSCI","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"rank-minimization-for-snapshot-compressive","repo_url":"https://github.com/Scientific-Research-Algorithm-Toolbox/SCI-algorithms","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"rank-minimization-for-snapshot-compressive","repo_url":"https://github.com/hust512/DeSCI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.07837","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}