{"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/acceleration-of-red-via-vector-extrapolation","title":"Acceleration of RED via Vector Extrapolation","arxiv_id":"1805.02158","date":"2018-05-06","proceeding":null,"authors":["Tao Hong","Yaniv Romano","Michael Elad"],"abstract":"Models play an important role in inverse problems, serving as the prior for\nrepresenting the original signal to be recovered. REgularization by Denoising\n(RED) is a recently introduced general framework for constructing such priors\nusing state-of-the-art denoising algorithms. Using RED, solving inverse\nproblems is shown to amount to an iterated denoising process. However, as the\ncomplexity of denoising algorithms is generally high, this might lead to an\noverall slow algorithm. In this paper, we suggest an accelerated technique\nbased on vector extrapolation (VE) to speed-up existing RED solvers. Numerical\nexperiments validate the obtained gain by VE, leading to a substantial savings\nin computations compared with the original fixed-point method.","url_abs":"http://arxiv.org/abs/1805.02158v2","url_pdf":"http://arxiv.org/pdf/1805.02158v2.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":"acceleration-of-red-via-vector-extrapolation","repo_url":"https://github.com/happyhongt/Acceleration-of-RED-via-Vector-Extrapolation","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}