{"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/hyperspectral-image-denoising-employing-a","title":"Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network","arxiv_id":"1806.00183","date":"2018-06-01","proceeding":null,"authors":["Qiangqiang Yuan","Qiang Zhang","Jie Li","Huanfeng Shen","Liangpei Zhang"],"abstract":"Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to\nimprove the performance of the subsequent HSI interpretation and applications.\nIn this paper, a novel deep learning-based method for this task is proposed, by\nlearning a non-linear end-to-end mapping between the noisy and clean HSIs with\na combined spatial-spectral deep convolutional neural network (HSID-CNN). Both\nthe spatial and spectral information are simultaneously assigned to the\nproposed network. In addition, multi-scale feature extraction and multi-level\nfeature representation are respectively employed to capture both the\nmulti-scale spatial-spectral feature and fuse the feature representations with\ndifferent levels for the final restoration. The simulated and real-data\nexperiments demonstrate that the proposed HSID-CNN outperforms many of the\nmainstream methods in both the quantitative evaluation indexes, visual effects,\nand HSI classification accuracy.","url_abs":"http://arxiv.org/abs/1806.00183v3","url_pdf":"http://arxiv.org/pdf/1806.00183v3.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":"hyperspectral-image-denoising-employing-a","repo_url":"https://github.com/WHUQZhang/HSID-CNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"hyperspectral-image-denoising-employing-a","repo_url":"https://github.com/rish-av/hsid-cnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"hyperspectral-image-denoising","task_name":"Hyperspectral Image Denoising"},{"task_slug":"image-denoising","task_name":"Image Denoising"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1806.00183","atlas_url":"https://app.syntology.ai/?focus=1806.00183","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}