{"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/perceptual-compressive-sensing","title":"Perceptual Compressive Sensing","arxiv_id":"1802.00176","date":"2018-02-01","proceeding":null,"authors":["Jiang Du","Xuemei Xie","Chenye Wang","Guangming Shi"],"abstract":"Compressive sensing (CS) works to acquire measurements at sub-Nyquist rate\nand recover the scene images. Existing CS methods always recover the scene\nimages in pixel level. This causes the smoothness of recovered images and lack\nof structure information, especially at a low measurement rate. To overcome\nthis drawback, in this paper, we propose perceptual CS to obtain high-level\nstructured recovery. Our task no longer focuses on pixel level. Instead, we\nwork to make a better visual effect. In detail, we employ perceptual loss,\ndefined on feature level, to enhance the structure information of the recovered\nimages. Experiments show that our method achieves better visual results with\nstronger structure information than existing CS methods at the same measurement\nrate.","url_abs":"http://arxiv.org/abs/1802.00176v2","url_pdf":"http://arxiv.org/pdf/1802.00176v2.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":"perceptual-compressive-sensing","repo_url":"https://github.com/jiang-du/Perceptual-CS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"compressive-sensing","task_name":"Compressive Sensing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}