{"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/iterative-residual-image-deconvolution","title":"Iterative Residual Image Deconvolution","arxiv_id":"1804.06042","date":"2018-04-17","proceeding":null,"authors":["Li Si-Yao","Dongwei Ren","Furong Zhao","Zijian Hu","Junfeng Li","Qian Yin"],"abstract":"Image deblurring, a.k.a. image deconvolution, recovers a clear image from\npixel superposition caused by blur degradation. Few deep convolutional neural\nnetworks (CNN) succeed in addressing this task. In this paper, we first\ndemonstrate that the minimum-mean-square-error (MMSE) solution to image\ndeblurring can be interestingly unfolded into a series of residual components.\nBased on this analysis, we propose a novel iterative residual deconvolution\n(IRD) algorithm. Further, IRD motivates us to take one step forward to design\nan explicable and effective CNN architecture for image deconvolution.\nSpecifically, a sequence of residual CNN units are deployed, whose intermediate\noutputs are then concatenated and integrated, resulting in concatenated\nresidual convolutional network (CRCNet). The experimental results demonstrate\nthat proposed CRCNet not only achieves better quantitative metrics but also\nrecovers more visually plausible texture details compared with state-of-the-art\nmethods.","url_abs":"http://arxiv.org/abs/1804.06042v2","url_pdf":"http://arxiv.org/pdf/1804.06042v2.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":"iterative-residual-image-deconvolution","repo_url":"https://github.com/lisiyaoATbnu/crcnet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deblurring","task_name":"Deblurring"},{"task_slug":"image-deblurring","task_name":"Image Deblurring"},{"task_slug":"image-deconvolution","task_name":"Image Deconvolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}