{"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/multi-channel-weighted-nuclear-norm","title":"Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising","arxiv_id":"1705.09912","date":"2017-05-28","proceeding":"ICCV 2017 10","authors":["Jun Xu","Lei Zhang","David Zhang","Xiangchu Feng"],"abstract":"Most of the existing denoising algorithms are developed for grayscale images,\nwhile it is not a trivial work to extend them for color image denoising because\nthe noise statistics in R, G, B channels can be very different for real noisy\nimages. In this paper, we propose a multi-channel (MC) optimization model for\nreal color image denoising under the weighted nuclear norm minimization (WNNM)\nframework. We concatenate the RGB patches to make use of the channel\nredundancy, and introduce a weight matrix to balance the data fidelity of the\nthree channels in consideration of their different noise statistics. The\nproposed MC-WNNM model does not have an analytical solution. We reformulate it\ninto a linear equality-constrained problem and solve it with the alternating\ndirection method of multipliers. Each alternative updating step has closed-form\nsolution and the convergence can be guaranteed. Extensive experiments on both\nsynthetic and real noisy image datasets demonstrate the superiority of the\nproposed MC-WNNM over state-of-the-art denoising methods.","url_abs":"http://arxiv.org/abs/1705.09912v2","url_pdf":"http://arxiv.org/pdf/1705.09912v2.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":[],"tasks":[{"task_slug":"color-image-denoising","task_name":"Color Image Denoising"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-denoising","task_name":"Image Denoising"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/denoising-on-darmstadt-noise-dataset","task":"Denoising","dataset":"Darmstadt Noise Dataset","model":"MCWNNM","rank_in_archive_order":5,"of":10,"metrics":{"PSNR":"37.38"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.09912","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}