{"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/non-local-meets-global-an-integrated-paradigm","title":"Non-local Meets Global: An Integrated Paradigm for Hyperspectral Denoising","arxiv_id":"1812.04243","date":"2018-12-11","proceeding":"CVPR 2019 6","authors":["Wei He","Quanming Yao","Chao Li","Naoto Yokoya","Qibin Zhao"],"abstract":"Non-local low-rank tensor approximation has been developed as a\nstate-of-the-art method for hyperspectral image (HSI) denoising. Unfortunately,\nwith more spectral bands for HSI, while the running time of these methods\nsignificantly increases, their denoising performance benefits little. In this\npaper, we claim that the HSI underlines a global spectral low-rank subspace,\nand the spectral subspaces of each full band patch groups should underlie this\nglobal low-rank subspace. This motivates us to propose a unified\nspatial-spectral paradigm for HSI denoising. As the new model is hard to\noptimize, we further propose an efficient algorithm for optimization, which is\nmotivated by alternating minimization. This is done by first learning a\nlow-dimensional projection and the related reduced image from the noisy HSI.\nThen, the non-local low-rank denoising and iterative regularization are\ndeveloped to refine the reduced image and projection, respectively. Finally,\nexperiments on synthetic and both real datasets demonstrate the superiority\nagainst the other state-of-the-arts HSI denoising methods.","url_abs":"http://arxiv.org/abs/1812.04243v2","url_pdf":"http://arxiv.org/pdf/1812.04243v2.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":"non-local-meets-global-an-integrated-paradigm","repo_url":"https://github.com/quanmingyao/NGMeet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"non-local-meets-global-an-integrated-paradigm","repo_url":"https://github.com/hust512/tensor_decoding_spectral_SCI_cameras","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"hyperspectral-image-denoising","task_name":"Hyperspectral Image Denoising"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.04243","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}