{"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/hybrid-noise-removal-in-hyperspectral-imagery","title":"Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient Network","arxiv_id":"1810.00495","date":"2018-10-01","proceeding":null,"authors":["Qiang Zhang","Qiangqiang Yuan","Jie Li","Xin-Xin Liu","Huanfeng Shen","Liangpei Zhang"],"abstract":"The existence of hybrid noise in hyperspectral images (HSIs) severely\ndegrades the data quality, reduces the interpretation accuracy of HSIs, and\nrestricts the subsequent HSIs applications. In this paper, the spatial-spectral\ngradient network (SSGN) is presented for mixed noise removal in HSIs. The\nproposed method employs a spatial-spectral gradient learning strategy, in\nconsideration of the unique spatial structure directionality of sparse noise\nand spectral differences with additional complementary information for better\nextracting intrinsic and deep features of HSIs. Based on a fully cascaded\nmulti-scale convolutional network, SSGN can simultaneously deal with the\ndifferent types of noise in different HSIs or spectra by the use of the same\nmodel. The simulated and real-data experiments undertaken in this study\nconfirmed that the proposed SSGN performs better at mixed noise removal than\nthe other state-of-the-art HSI denoising algorithms, in evaluation indices,\nvisual assessments, and time consumption.","url_abs":"http://arxiv.org/abs/1810.00495v3","url_pdf":"http://arxiv.org/pdf/1810.00495v3.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":"hybrid-noise-removal-in-hyperspectral-imagery","repo_url":"https://github.com/WHUQZhang/SSGN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}