{"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/image-restoration-by-iterative-denoising-and","title":"Image Restoration by Iterative Denoising and Backward Projections","arxiv_id":"1710.06647","date":"2017-10-18","proceeding":null,"authors":["Tom Tirer","Raja Giryes"],"abstract":"Inverse problems appear in many applications, such as image deblurring and\ninpainting. The common approach to address them is to design a specific\nalgorithm for each problem. The Plug-and-Play (P&P) framework, which has been\nrecently introduced, allows solving general inverse problems by leveraging the\nimpressive capabilities of existing denoising algorithms. While this fresh\nstrategy has found many applications, a burdensome parameter tuning is often\nrequired in order to obtain high-quality results. In this work, we propose an\nalternative method for solving inverse problems using off-the-shelf denoisers,\nwhich requires less parameter tuning. First, we transform a typical cost\nfunction, composed of fidelity and prior terms, into a closely related, novel\noptimization problem. Then, we propose an efficient minimization scheme with a\nplug-and-play property, i.e., the prior term is handled solely by a denoising\noperation. Finally, we present an automatic tuning mechanism to set the\nmethod's parameters. We provide a theoretical analysis of the method, and\nempirically demonstrate its competitiveness with task-specific techniques and\nthe P&P approach for image inpainting and deblurring.","url_abs":"http://arxiv.org/abs/1710.06647v4","url_pdf":"http://arxiv.org/pdf/1710.06647v4.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":"image-restoration-by-iterative-denoising-and","repo_url":"https://github.com/tomtirer/IDBP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"image-restoration-by-iterative-denoising-and","repo_url":"https://github.com/tirer-lab/ddpg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"deblurring","task_name":"Deblurring"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-deblurring","task_name":"Image Deblurring"},{"task_slug":"image-inpainting","task_name":"Image Inpainting"},{"task_slug":"image-restoration","task_name":"Image Restoration"}],"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}