Deep Content-Dependent 3-D Convolutional Sparse Coding for Hyperspectral Image Denoising

Despite the significant successes in hyperspectral image (HSI) denoising, pure data-driven HSI denoising networks still suffer from limited understanding of inference. Deep unfolding (DU) is a feasible way to improve the interpretability of deep network. However, the specialized spatial-spectral DU...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE journal of selected topics in applied earth observations and remote sensing Vol. 17; pp. 4125 - 4138
Main Authors:	Yin, Haitao, Chen, Hao
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	3-D convolution Adaptability Convolution Convolutional codes convolutional sparse coding deep network deep unfolding Encoding Filters Hyperspectral image denoising Hyperspectral imaging Image coding Noise reduction separable convolution Tensors Three-dimensional displays
ISSN:	1939-1404, 2151-1535
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Despite the significant successes in hyperspectral image (HSI) denoising, pure data-driven HSI denoising networks still suffer from limited understanding of inference. Deep unfolding (DU) is a feasible way to improve the interpretability of deep network. However, the specialized spatial-spectral DU methods are seldom studied, and the simple spatial-spectral extension leads to unpleasant spectral distortion. To tackle these issues, we first propose a content-dependent 3-D convolutional sparse coding (CD-CSC) to jointly represent spatial-spectral feature. Specifically, the 3-D filters used in CD-CSC for each HSI are unique, which are determined by linear combination of base 3-D filters. Then, we develop a novel CD-CSC-inspired DU network for HSI denoising, called CD-CSCNet. Furthermore, by exploiting the lightweight of separable convolution and the adaptability of hypernetwork, we design a separable content-dependent 3D Convolution (SCD-Conv) to carry out CD-CSCNet. SCD-Conv not only reduces computational complexity, but also can be viewed as the convolutional sparse coding with spatial and spectral dictionaries. Extensive experimental results on the ICVL, Zhuhai-1 OHS-3C, and GaoFen-5 datasets demonstrate that CD-CSCNet outperforms several recent pure data-driven and DU-based networks quantitatively and visually.
AbstractList	Despite the significant successes in hyperspectral image (HSI) denoising, pure data-driven HSI denoising networks still suffer from limited understanding of inference. Deep unfolding (DU) is a feasible way to improve the interpretability of deep network. However, the specialized spatial-spectral DU methods are seldom studied, and the simple spatial-spectral extension leads to unpleasant spectral distortion. To tackle these issues, we first propose a content-dependent 3-D convolutional sparse coding (CD-CSC) to jointly represent spatial-spectral feature. Specifically, the 3-D filters used in CD-CSC for each HSI are unique, which are determined by linear combination of base 3-D filters. Then, we develop a novel CD-CSC-inspired DU network for HSI denoising, called CD-CSCNet. Furthermore, by exploiting the lightweight of separable convolution and the adaptability of hypernetwork, we design a separable content-dependent 3D Convolution (SCD-Conv) to carry out CD-CSCNet. SCD-Conv not only reduces computational complexity, but also can be viewed as the convolutional sparse coding with spatial and spectral dictionaries. Extensive experimental results on the ICVL, Zhuhai-1 OHS-3C, and GaoFen-5 datasets demonstrate that CD-CSCNet outperforms several recent pure data-driven and DU-based networks quantitatively and visually.
Author	Yin, Haitao Chen, Hao
Author_xml	– sequence: 1 givenname: Haitao orcidid: 0000-0003-2975-2188 surname: Yin fullname: Yin, Haitao email: haitaoyin@njupt.edu.cn organization: College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China – sequence: 2 givenname: Hao orcidid: 0009-0007-0600-4351 surname: Chen fullname: Chen, Hao email: ch1263152934@163.com organization: College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China
BookMark	eNp9UU1rGzEUFCGFOml_QXNYyHkdPUm7Wh2DnTQOgUKdQG5C1j4Zmc1qI60D_veRuymEHnqSmDfzPmbOyGkfeiTkB9A5AFVX9-vH69_rOaNMzDmvpOTshMwYVFBCxatTMgPFVQmCiq_kLKUdpTWTis_I8xJxKBahH7EfyyUO2Lf5V_ByeUTfQrcffehNV6wHExNmsPX9tnAhFneHAWMa0I4x11cvZovFEvvgU2Z8I1-c6RJ-_3jPydPtzePirnz49XO1uH4oraBqLGuLtqLYOCskNgLkpgXVOCeZNI3KVYSNqylK1wpHkUPGrMs3tVQKtJyfk9XUtw1mp4foX0w86GC8_gOEuNUmjt52qE0tWgBpFZg8zbRNtRGsYYaK7EWzsbnX5dRriOF1j2nUu7CP-fikmWI1qxWlKrPUxLIxpBTRaetHc3Qp--A7DVQfQ9FTKPoYiv4IJWv5P9q_G_9fdTGpPCJ-UgjIS1H-Donpms4
CODEN	IJSTHZ
CitedBy_id	crossref_primary_10_1016_j_infrared_2025_106139 crossref_primary_10_1109_TGRS_2025_3603390 crossref_primary_10_3389_fnins_2024_1516514 crossref_primary_10_1016_j_asr_2025_09_028 crossref_primary_10_1038_s41598_025_13857_9
Cites_doi	10.1109/JSTSP.2018.2873148 10.1109/LGRS.2016.2518218 10.1109/TIP.2015.2404782 10.1109/IGARSS.2012.6351286 10.1109/CVPR52729.2023.00562 10.1109/TGRS.2022.3202714 10.1109/LGRS.2017.2700406 10.1109/TGRS.2013.2284280 10.1109/TGRS.2021.3110769 10.1109/TGRS.2018.2859203 10.1109/LGRS.2022.3192912 10.1109/TGRS.2021.3069241 10.1109/MGRS.2013.2244672 10.1109/JSTARS.2023.3283342 10.1109/TGRS.2010.2075937 10.1109/MGRS.2021.3121761 10.1109/cvpr.2016.187 10.1109/TGRS.2021.3131878 10.1109/JSTARS.2017.2779539 10.1109/JSTARS.2023.3257051 10.1109/TGRS.2015.2452812 10.1109/JSTARS.2018.2791718 10.1109/JSTARS.2023.3312523 10.1109/TGRS.2022.3156646 10.1109/TGRS.2020.3024623 10.1109/cvpr.2014.377 10.1109/JSTARS.2020.3024903 10.1109/TIP.2012.2210725 10.1109/JSTARS.2019.2902430 10.1109/JSTARS.2023.3294619 10.1109/TGRS.2016.2547879 10.1109/TGRS.2018.2865197 10.1109/JSTARS.2023.3288521 10.1109/JSTARS.2021.3138564 10.1109/TCI.2018.2840334 10.1109/JSTARS.2022.3185657 10.1109/tgrs.2022.3177719 10.1109/JSTARS.2022.3218820 10.1109/TGRS.2019.2946050 10.1109/JSTARS.2023.3282975 10.1109/TGRS.2019.2897316 10.1109/TGRS.2022.3142988 10.1109/cvpr.2017.625 10.1109/TCYB.2021.3051656 10.1109/TCYB.2017.2677944 10.1109/TGRS.2012.2225065 10.1109/TCYB.2020.2983102 10.1109/TGRS.2017.2771155 10.1109/LGRS.2021.3112038 10.1109/TGRS.2021.3071799 10.1109/TIP.2019.2928627 10.1109/TGRS.2005.860982 10.1109/JSTARS.2017.2714338 10.1109/JSTARS.2023.3301149 10.1109/TSP.2020.2971441 10.1109/JSTARS.2023.3319587 10.1109/TNNLS.2021.3112577 10.1109/JSTARS.2018.2805290 10.1109/TGRS.2012.2185054 10.1109/TNNLS.2020.2978756 10.1109/TGRS.2022.3217097
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024
DBID	97E ESBDL RIA RIE AAYXX CITATION 7UA 8FD C1K F1W FR3 H8D H96 KR7 L.G L7M DOA
DOI	10.1109/JSTARS.2024.3357732
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE/IET Electronic Library CrossRef Water Resources Abstracts Technology Research Database Environmental Sciences and Pollution Management ASFA: Aquatic Sciences and Fisheries Abstracts Engineering Research Database Aerospace Database Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources Civil Engineering Abstracts Aquatic Science & Fisheries Abstracts (ASFA) Professional Advanced Technologies Database with Aerospace DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef Aerospace Database Civil Engineering Abstracts Aquatic Science & Fisheries Abstracts (ASFA) Professional Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources Technology Research Database ASFA: Aquatic Sciences and Fisheries Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Water Resources Abstracts Environmental Sciences and Pollution Management
DatabaseTitleList	Aerospace Database
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Geology
EISSN	2151-1535
EndPage	4138
ExternalDocumentID	oai_doaj_org_article_a64d117c91ac47ad85b4282a042798bc 10_1109_JSTARS_2024_3357732 10412620
Genre	orig-research
GrantInformation_xml	– fundername: Jiangsu Qing Lan Project – fundername: National Natural Science Foundation of China grantid: 61971237 funderid: 10.13039/501100001809 – fundername: China Computer Federation-Baidu Open Fund grantid: 202215
GroupedDBID	0R~ 29I 4.4 5GY 5VS 6IK 97E AAFWJ AAJGR AASAJ AAWTH ABAZT ABVLG ACIWK AENEX AETIX AFPKN AFRAH AGSQL ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ DU5 EBS EJD ESBDL GROUPED_DOAJ HZ~ IFIPE IPLJI JAVBF M43 O9- OCL OK1 RIA RIE RNS AAYXX CITATION 7UA 8FD C1K F1W FR3 H8D H96 KR7 L.G L7M
ID	FETCH-LOGICAL-c409t-6cec50e8fc47e8417bd198ff727a896cee1bf60e7fd4f0e3196ccf535d074ec33
IEDL.DBID	DOA
ISICitedReferencesCount	5
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001162787700004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1939-1404
IngestDate	Fri Oct 03 12:40:01 EDT 2025 Sat Sep 06 06:23:52 EDT 2025 Sat Nov 29 08:11:23 EST 2025 Tue Nov 18 20:45:57 EST 2025 Wed Aug 27 01:42:10 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Language	English
License	https://creativecommons.org/licenses/by-nc-nd/4.0
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c409t-6cec50e8fc47e8417bd198ff727a896cee1bf60e7fd4f0e3196ccf535d074ec33
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-2975-2188 0009-0007-0600-4351
OpenAccessLink	https://doaj.org/article/a64d117c91ac47ad85b4282a042798bc
PQID	2926269009
PQPubID	75722
PageCount	14
ParticipantIDs	doaj_primary_oai_doaj_org_article_a64d117c91ac47ad85b4282a042798bc ieee_primary_10412620 crossref_primary_10_1109_JSTARS_2024_3357732 proquest_journals_2926269009 crossref_citationtrail_10_1109_JSTARS_2024_3357732
PublicationCentury	2000
PublicationDate	20240000 2024-00-00 20240101 2024-01-01
PublicationDateYYYYMMDD	2024-01-01
PublicationDate_xml	– year: 2024 text: 20240000
PublicationDecade	2020
PublicationPlace	Piscataway
PublicationPlace_xml	– name: Piscataway
PublicationTitle	IEEE journal of selected topics in applied earth observations and remote sensing
PublicationTitleAbbrev	JSTARS
PublicationYear	2024
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref12 ref56 ref15 ref59 ref14 ref58 ref53 ref52 ref11 ref55 ref10 ref54 ref17 ref16 ref19 ref18 ref51 ref50 Bodrito (ref62) 2021; 34 ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref1 ref39 ref38 ref24 Gregor (ref57) 2010 ref23 ref26 ref25 ref20 ref63 ref22 ref21 ref28 ref27 ref29 ref60 ref61
References_xml	– ident: ref32 doi: 10.1109/JSTSP.2018.2873148 – ident: ref16 doi: 10.1109/LGRS.2016.2518218 – ident: ref17 doi: 10.1109/TIP.2015.2404782 – ident: ref12 doi: 10.1109/IGARSS.2012.6351286 – ident: ref56 doi: 10.1109/CVPR52729.2023.00562 – ident: ref40 doi: 10.1109/TGRS.2022.3202714 – ident: ref23 doi: 10.1109/LGRS.2017.2700406 – ident: ref20 doi: 10.1109/TGRS.2013.2284280 – ident: ref22 doi: 10.1109/TGRS.2021.3110769 – ident: ref45 doi: 10.1109/TGRS.2018.2859203 – ident: ref19 doi: 10.1109/LGRS.2022.3192912 – ident: ref47 doi: 10.1109/TGRS.2021.3069241 – start-page: 399 volume-title: Proc. Int. Conf. Mach. Learn. year: 2010 ident: ref57 article-title: Learning fast approximations of sparse coding – ident: ref1 doi: 10.1109/MGRS.2013.2244672 – ident: ref5 doi: 10.1109/JSTARS.2023.3283342 – ident: ref11 doi: 10.1109/TGRS.2010.2075937 – ident: ref9 doi: 10.1109/MGRS.2021.3121761 – ident: ref27 doi: 10.1109/cvpr.2016.187 – ident: ref59 doi: 10.1109/TGRS.2021.3131878 – ident: ref41 doi: 10.1109/JSTARS.2017.2779539 – volume: 34 start-page: 5430 volume-title: Proc. Adv. Neural Inf. Process. Syst. year: 2021 ident: ref62 article-title: A trainable spectral-spatial sparse coding model for hyperspectral image restoration – ident: ref54 doi: 10.1109/JSTARS.2023.3257051 – ident: ref39 doi: 10.1109/TGRS.2015.2452812 – ident: ref29 doi: 10.1109/JSTARS.2018.2791718 – ident: ref8 doi: 10.1109/JSTARS.2023.3312523 – ident: ref52 doi: 10.1109/TGRS.2022.3156646 – ident: ref38 doi: 10.1109/TGRS.2020.3024623 – ident: ref28 doi: 10.1109/cvpr.2014.377 – ident: ref7 doi: 10.1109/JSTARS.2020.3024903 – ident: ref14 doi: 10.1109/TIP.2012.2210725 – ident: ref6 doi: 10.1109/JSTARS.2019.2902430 – ident: ref51 doi: 10.1109/JSTARS.2023.3294619 – ident: ref21 doi: 10.1109/TGRS.2016.2547879 – ident: ref46 doi: 10.1109/TGRS.2018.2865197 – ident: ref4 doi: 10.1109/JSTARS.2023.3288521 – ident: ref58 doi: 10.1109/JSTARS.2021.3138564 – ident: ref63 doi: 10.1109/TCI.2018.2840334 – ident: ref18 doi: 10.1109/JSTARS.2022.3185657 – ident: ref36 doi: 10.1109/tgrs.2022.3177719 – ident: ref50 doi: 10.1109/JSTARS.2022.3218820 – ident: ref35 doi: 10.1109/TGRS.2019.2946050 – ident: ref3 doi: 10.1109/JSTARS.2023.3282975 – ident: ref30 doi: 10.1109/TGRS.2019.2897316 – ident: ref37 doi: 10.1109/TGRS.2022.3142988 – ident: ref43 doi: 10.1109/cvpr.2017.625 – ident: ref44 doi: 10.1109/TCYB.2021.3051656 – ident: ref25 doi: 10.1109/TCYB.2017.2677944 – ident: ref13 doi: 10.1109/TGRS.2012.2225065 – ident: ref33 doi: 10.1109/TCYB.2020.2983102 – ident: ref26 doi: 10.1109/TGRS.2017.2771155 – ident: ref49 doi: 10.1109/LGRS.2021.3112038 – ident: ref48 doi: 10.1109/TGRS.2021.3071799 – ident: ref60 doi: 10.1109/TIP.2019.2928627 – ident: ref10 doi: 10.1109/TGRS.2005.860982 – ident: ref34 doi: 10.1109/JSTARS.2017.2714338 – ident: ref42 doi: 10.1109/JSTARS.2023.3301149 – ident: ref31 doi: 10.1109/TSP.2020.2971441 – ident: ref2 doi: 10.1109/JSTARS.2023.3319587 – ident: ref61 doi: 10.1109/TNNLS.2021.3112577 – ident: ref24 doi: 10.1109/JSTARS.2018.2805290 – ident: ref15 doi: 10.1109/TGRS.2012.2185054 – ident: ref55 doi: 10.1109/TNNLS.2020.2978756 – ident: ref53 doi: 10.1109/TGRS.2022.3217097
SSID	ssj0062793
Score	2.379606
Snippet	Despite the significant successes in hyperspectral image (HSI) denoising, pure data-driven HSI denoising networks still suffer from limited understanding of...
SourceID	doaj proquest crossref ieee
SourceType	Open Website Aggregation Database Enrichment Source Index Database Publisher
StartPage	4125
SubjectTerms	3-D convolution Adaptability Convolution Convolutional codes convolutional sparse coding deep network deep unfolding Encoding Filters Hyperspectral image denoising Hyperspectral imaging Image coding Noise reduction separable convolution Tensors Three-dimensional displays
SummonAdditionalLinks	– databaseName: IEEE/IET Electronic Library dbid: RIE link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dTxQxEG-EaOILCmI4RLMPPFLcbr8fkRMhIcSIkHtruu1sQqJ7l2Mh4b932u0RidHEt00_drs7M-382p3fELIvM4lU7WlIsR8iGEWtFEBbAyzHXtucJeL6XF9cmNnMfi3B6jkWBgDyz2dwmC7zWX6ch7u0VYYWLlgiUF8ja1qrMVhrNe2qRmeGXXRILE2cMYViiNX2I-r40bdLBIONOORcas2bJ8tQZusv6VX-mJPzQnPy6j-H-JpsFI-yOhpVYJM8g36LvPiSM_Y-vCGzKcCiyiRU_UCnJeftUHE6TaX3RfXwDpcLBLmAhWk5q9CZrU4RpI6xmEusP_uJc081hX5-kzYYtsnVyefvx6e0pFOgAUHcQFWAIGswXRAajGC6jcyarkMPxhuLtcDaTtWguyi6GpJthtBJLiO6GRA4f0vW-3kPO6RqEWioKHVrmiiU514ZwZX1UXqN_qadkGb1dV0oXOMp5cUPlzFHbd0oEpdE4opIJuTgsdNipNr4d_NPSWyPTRNPdi5Aebhids4rERnTwTKPb-2jkS3ircanDCPWtGFCtpMMf3veKL4J2VtpgStGfeuaxK2oLHqlu3_p9o68TEMct2j2yPqwvIP35Hm4H25ulx-yvv4CK1zktw priority: 102 providerName: IEEE
Title	Deep Content-Dependent 3-D Convolutional Sparse Coding for Hyperspectral Image Denoising
URI	https://ieeexplore.ieee.org/document/10412620 https://www.proquest.com/docview/2926269009 https://doaj.org/article/a64d117c91ac47ad85b4282a042798bc
Volume	17
WOSCitedRecordID	wos001162787700004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2151-1535 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062793 issn: 1939-1404 databaseCode: DOA dateStart: 20200101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 2151-1535 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062793 issn: 1939-1404 databaseCode: RIE dateStart: 20080101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3fb9MwELbQBBIv04ChFUaVBx4xi-Pfj4VShoQmRAH1zXIcR5o0sqoLk_bfc2e7VRESe-HVduLk7ny-c3LfR8hrmUCkak8D1n6IYBS1UkTamshS7bVNLBE_PuuLC7Na2S97VF_4T1iGB86CO_NKdIzpYJkPQvvOyBYi5sYjR4Q1bUDvW2u7TaayD1bQx_P3ZEsRQKbgDbHanoHBz74uITNsxFvOpda8-WNPStD9hWvlLweddp3FETks4WI1y4_5hDyIw1Py6GOi4717RlbzGNdVQpgaRjovhLZjxekcW2-LXcEdlmvIYCM04l5VQaRanUMGmgstN9D_6Sc4lmoeh-tLPD04Jt8XH769P6eFK4EGyNBGqkIMso6mByFFI5huO2ZN30N44o2F3sjaXtVR953o64gLL4RectlBDBED58_JwXA9xBNStZBFqE7q1jSdUJ57ZQRX1nfSawgm7YQ0W2m5UIDEkc_iyqWEorYui9ihiF0R8YS82V20zjga_x7-DtWwG4og2KkBTMMV03D3mcaEHKMS9-YTDPH3J-R0q1VXVuyNaxA4UVkIOV_8j7lfksf4Pvmw5pQcjJtf8RV5GG7Hy5vNNBnrNBUb_gYlTeft
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwELaggODCs4iFAjlwxCWO38fCUrZiWSFa0N4sx55IlUp2tU0r8e8ZO94KhEDiFvmROJkZez478w0hr2Qmkao9DSn2QwSjqJUCaGuA5dhrm7NEfJvrxcIsl_ZzCVbPsTAAkH8-g_10mc_y4ypcpK0ytHDBEoH6dXJDCtHUY7jWduJVjc4cu-iSWJpYYwrJEKvtG9Tygy_HCAcbsc-51Jo3vy1Ema-_JFj5Y1bOS83hvf8c5H1yt_iU1cGoBA_INegfklsfcs7eH4_IcgqwrjINVT_Qacl6O1ScTlPpZVE-vMPxGmEuYGFa0Cp0Z6sZwtQxGnOD9UffcfapptCvTtMWwy75evj-5N2MloQKNCCMG6gKEGQNpgtCgxFMt5FZ03Xow3hjsRZY26kadBdFV0OyzhA6yWVERwMC54_JTr_q4QmpWoQaKkrdmiYK5blXRnBlfZReo8dpJ6TZfl0XCtt4Snpx5jLqqK0bReKSSFwRyYS8vuq0Hsk2_t38bRLbVdPElJ0LUB6uGJ7zSkTGdLDM41v7aGSLiKvxKceINW2YkN0kw1-eN4pvQva2WuCKWZ-7JrErKot-6dO_dHtJbs9OPs3d_Gjx8Rm5k4Y7btjskZ1hcwHPyc1wOZyeb15k3f0JNvrn_g
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Deep+Content-Dependent+3-D+Convolutional+Sparse+Coding+for+Hyperspectral+Image+Denoising&rft.jtitle=IEEE+journal+of+selected+topics+in+applied+earth+observations+and+remote+sensing&rft.au=Yin%2C+Haitao&rft.au=Chen%2C+Hao&rft.date=2024&rft.pub=IEEE&rft.issn=1939-1404&rft.volume=17&rft.spage=4125&rft.epage=4138&rft_id=info:doi/10.1109%2FJSTARS.2024.3357732&rft.externalDocID=10412620
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1939-1404&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1939-1404&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1939-1404&client=summon