{"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/unrolled-optimization-with-deep-priors","title":"Unrolled Optimization with Deep Priors","arxiv_id":"1705.08041","date":"2017-05-22","proceeding":null,"authors":["Steven Diamond","Vincent Sitzmann","Felix Heide","Gordon Wetzstein"],"abstract":"A broad class of problems at the core of computational imaging, sensing, and\nlow-level computer vision reduces to the inverse problem of extracting latent\nimages that follow a prior distribution, from measurements taken under a known\nphysical image formation model. Traditionally, hand-crafted priors along with\niterative optimization methods have been used to solve such problems. In this\npaper we present unrolled optimization with deep priors, a principled framework\nfor infusing knowledge of the image formation into deep networks that solve\ninverse problems in imaging, inspired by classical iterative methods. We show\nthat instances of the framework outperform the state-of-the-art by a\nsubstantial margin for a wide variety of imaging problems, such as denoising,\ndeblurring, and compressed sensing magnetic resonance imaging (MRI). Moreover,\nwe conduct experiments that explain how the framework is best used and why it\noutperforms previous methods.","url_abs":"http://arxiv.org/abs/1705.08041v2","url_pdf":"http://arxiv.org/pdf/1705.08041v2.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":"unrolled-optimization-with-deep-priors","repo_url":"https://github.com/Zhengqi-Wu/Unrolled-optimization-with-deep-priors","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"unrolled-optimization-with-deep-priors","repo_url":"https://github.com/hershey890/optimization_deep_priors","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"deblurring","task_name":"Deblurring"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"compressed-sensing","task_name":"compressed sensing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.08041","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}