{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T22:25:32Z","timestamp":1776896732507,"version":"3.51.2"},"reference-count":59,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T00:00:00Z","timestamp":1673568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Project of China","award":["2020AAA0105600"],"award-info":[{"award-number":["2020AAA0105600"]}]},{"name":"National Key Research and Development Project of China","award":["62006183"],"award-info":[{"award-number":["62006183"]}]},{"name":"National Key Research and Development Project of China","award":["xhj032021017-04"],"award-info":[{"award-number":["xhj032021017-04"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2020AAA0105600"],"award-info":[{"award-number":["2020AAA0105600"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62006183"],"award-info":[{"award-number":["62006183"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["xhj032021017-04"],"award-info":[{"award-number":["xhj032021017-04"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2020AAA0105600"],"award-info":[{"award-number":["2020AAA0105600"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["62006183"],"award-info":[{"award-number":["62006183"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["xhj032021017-04"],"award-info":[{"award-number":["xhj032021017-04"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Although existing hyperspectral image (HSI) denoising methods have exhibited promising performance in synthetic noise removal, they are seriously restricted in real-world scenarios with complicated noises. The major reason is that model-based methods largely rely on the noise type assumption and parameter setting, and learning-based methods perform poorly in generalizability due to the scarcity of real-world clean\u2013noisy data pairs. To overcome this long-standing challenge, we propose a novel denoising method with degradation information learning (termed DIBD), which attempts to approximate the joint distribution of the clean\u2013noisy HSI pairs in a Bayesian framework. Specifically, our framework learns the mappings of noisy-to-clean and clean-to-noisy in a priority dual regression scheme. We develop more comprehensive auxiliary information to simplify the joint distribution approximation process instead of only estimating noise intensity. Our method can leverage both labeled synthetic and unlabeled real data for learning. Extensive experiments show that the proposed DIBD achieves state-of-the-art performance on synthetic datasets and has better generalization to real-world HSIs. The source code will be available to the public.<\/jats:p>","DOI":"10.3390\/rs15020490","type":"journal-article","created":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T04:06:47Z","timestamp":1673842007000},"page":"490","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Blind Hyperspectral Image Denoising with Degradation Information Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5025-3941","authenticated-orcid":false,"given":"Xing","family":"Wei","sequence":"first","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0469-528X","authenticated-orcid":false,"given":"Jiahua","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihong","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,13]]},"reference":[{"key":"ref_1","first-page":"5501916","article-title":"Feedback attention-based dense CNN for hyperspectral image classification","volume":"60","author":"Yu","year":"2021","journal-title":"IEEE Trans. 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