{"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/deepred-deep-image-prior-powered-by-red","title":"DeepRED: Deep Image Prior Powered by RED","arxiv_id":"1903.10176","date":"2019-03-25","proceeding":null,"authors":["Gary Mataev","Michael Elad","Peyman Milanfar"],"abstract":"Inverse problems in imaging are extensively studied, with a variety of strategies, tools, and theory that have been accumulated over the years. Recently, this field has been immensely influenced by the emergence of deep-learning techniques. One such contribution, which is the focus of this paper, is the Deep Image Prior (DIP) work by Ulyanov, Vedaldi, and Lempitsky (2018). DIP offers a new approach towards the regularization of inverse problems, obtained by forcing the recovered image to be synthesized from a given deep architecture. While DIP has been shown to be quite an effective unsupervised approach, its results still fall short when compared to state-of-the-art alternatives. In this work, we aim to boost DIP by adding an explicit prior, which enriches the overall regularization effect in order to lead to better-recovered images. More specifically, we propose to bring-in the concept of Regularization by Denoising (RED), which leverages existing denoisers for regularizing inverse problems. Our work shows how the two (DIP and RED) can be merged into a highly effective unsupervised recovery process while avoiding the need to differentiate the chosen denoiser, and leading to very effective results, demonstrated for several tested problems.","url_abs":"https://arxiv.org/abs/1903.10176v3","url_pdf":"https://arxiv.org/pdf/1903.10176v3.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":"deepred-deep-image-prior-powered-by-red","repo_url":"https://github.com/GaryMataev/DeepRED","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-super-resolution","task_name":"Image Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-super-resolution-on-set14-4x-upscaling","task":"Image Super-Resolution","dataset":"Set14 - 4x upscaling","model":"DeepRED","rank_in_archive_order":89,"of":104,"metrics":{"PSNR":"27.63"},"uses_additional_data":false},{"leaderboard":"/sota/image-super-resolution-on-set14-8x-upscaling","task":"Image Super-Resolution","dataset":"Set14 - 8x upscaling","model":"DeepRED","rank_in_archive_order":7,"of":7,"metrics":{"PSNR":"24.28"},"uses_additional_data":false},{"leaderboard":"/sota/image-super-resolution-on-set5-8x-upscaling","task":"Image Super-Resolution","dataset":"Set5 - 8x upscaling","model":"DeepRED","rank_in_archive_order":7,"of":8,"metrics":{"PSNR":"26.04"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1903.10176","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}