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on deep learning and a total variation (TV) prior. The method minimizes the first-order moment distance between the deep prior of a Fast and Flexible Denoising Convolutional Neural Network (FFDNet) and the Enhanced 3D TV (E3DTV) prior, obtaining dual priors that complement and reinforce each other\u2019s advantages. Specifically, the original HSI is initially processed with a random binary sparse observation matrix to achieve a sparse representation. Subsequently, the plug-and-play (PnP) algorithm is employed within the framework of generalized alternating projection (GAP) to denoise the sparsely represented HSI. Experimental results demonstrate that, compared to existing methods, this method shows significant advantages in both quantitative and qualitative assessments, effectively enhancing the quality of HSIs.<\/jats:p>","DOI":"10.3390\/rs16122071","type":"journal-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T10:43:42Z","timestamp":1717757022000},"page":"2071","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Hyperspectral Image Denoising Based on Deep and Total Variation Priors"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3825-6365","authenticated-orcid":false,"given":"Peng","family":"Wang","sequence":"first","affiliation":[{"name":"Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains, Zhengzhou 450052, China"},{"name":"Key Laboratory of Spatiotemporal Perception and Intelligent Processing, Ministry of Natural Resources, Zhengzhou 450052, China"},{"name":"College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}]},{"given":"Tianman","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6886-1179","authenticated-orcid":false,"given":"Yiming","family":"Chen","sequence":"additional","affiliation":[{"name":"National Dam Safety Research Center, Wuhan 430010, China"}]},{"given":"Lihua","family":"Ge","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}]},{"given":"Xiaoyi","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}]},{"given":"Liguo","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2256","DOI":"10.1109\/TGRS.2020.3004353","article-title":"Super-Resolution Mapping Based on Spatial\u2013Spectral Correlation for Spectral Imagery","volume":"59","author":"Wang","year":"2021","journal-title":"IEEE Trans. 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