{"id":"https://openalex.org/W4294643312","doi":"https://doi.org/10.1109/tgrs.2022.3204049","title":"GJTD-LR: A Trainable Grouped Joint Tensor Dictionary With Low-Rank Prior for Single Hyperspectral Image Super-Resolution","display_name":"GJTD-LR: A Trainable Grouped Joint Tensor Dictionary With Low-Rank Prior for Single Hyperspectral Image Super-Resolution","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4294643312","doi":"https://doi.org/10.1109/tgrs.2022.3204049"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2022.3204049","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3204049","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100331585","display_name":"Cong Liu","orcid":"https://orcid.org/0000-0002-3034-4504"},"institutions":[{"id":"https://openalex.org/I148128674","display_name":"University of Shanghai for Science and Technology","ror":"https://ror.org/00ay9v204","country_code":"CN","type":"education","lineage":["https://openalex.org/I148128674"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Cong Liu","raw_affiliation_strings":["School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-3034-4504","affiliations":[{"raw_affiliation_string":"School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China","institution_ids":["https://openalex.org/I148128674"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078689432","display_name":"Zhihao Fan","orcid":"https://orcid.org/0000-0002-9910-7937"},"institutions":[{"id":"https://openalex.org/I148128674","display_name":"University of Shanghai for Science and Technology","ror":"https://ror.org/00ay9v204","country_code":"CN","type":"education","lineage":["https://openalex.org/I148128674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihao Fan","raw_affiliation_strings":["School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China","institution_ids":["https://openalex.org/I148128674"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060120202","display_name":"Guixu Zhang","orcid":"https://orcid.org/0000-0003-4720-6607"},"institutions":[{"id":"https://openalex.org/I143593769","display_name":"East China University of Science and Technology","ror":"https://ror.org/01vyrm377","country_code":"CN","type":"education","lineage":["https://openalex.org/I143593769"]},{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guixu Zhang","raw_affiliation_strings":["Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Computer Science and Technology, East China Normal University, Shanghai, China","School of Computer Science and Technology, and the Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, East China Normal University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-4720-6607","affiliations":[{"raw_affiliation_string":"Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Computer Science and Technology, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065","https://openalex.org/I143593769"]},{"raw_affiliation_string":"School of Computer Science and Technology, and the Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100331585"],"corresponding_institution_ids":["https://openalex.org/I148128674"],"apc_list":null,"apc_paid":null,"fwci":1.7347,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.85876356,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"17"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8264063596725464},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6411424279212952},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6372307538986206},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6298559904098511},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5995875000953674},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.5603659152984619},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.47537484765052795},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.45358824729919434},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.4232962131500244},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.422040730714798},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.40127304196357727},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.365581214427948}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8264063596725464},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6411424279212952},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6372307538986206},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6298559904098511},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5995875000953674},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.5603659152984619},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.47537484765052795},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.45358824729919434},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.4232962131500244},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.422040730714798},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.40127304196357727},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.365581214427948},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2022.3204049","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3204049","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1583071013","display_name":null,"funder_award_id":"61703278","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W1032148204","https://openalex.org/W1885185971","https://openalex.org/W2024165284","https://openalex.org/W2030927653","https://openalex.org/W2043571470","https://openalex.org/W2055225600","https://openalex.org/W2080875060","https://openalex.org/W2084225223","https://openalex.org/W2087263574","https://openalex.org/W2088254198","https://openalex.org/W2117865218","https://openalex.org/W2121058967","https://openalex.org/W2126025632","https://openalex.org/W2157785665","https://openalex.org/W2159269332","https://openalex.org/W2187351272","https://openalex.org/W2278837653","https://openalex.org/W2464748116","https://openalex.org/W2494274259","https://openalex.org/W2615706402","https://openalex.org/W2752716059","https://openalex.org/W2762772695","https://openalex.org/W2767522909","https://openalex.org/W2773850265","https://openalex.org/W2792144524","https://openalex.org/W2898062170","https://openalex.org/W2908833896","https://openalex.org/W2910457605","https://openalex.org/W2919715196","https://openalex.org/W2945202593","https://openalex.org/W2945242228","https://openalex.org/W2962907479","https://openalex.org/W2963885538","https://openalex.org/W3007332620","https://openalex.org/W3016106731","https://openalex.org/W3025440211","https://openalex.org/W3026587394","https://openalex.org/W3027112496","https://openalex.org/W3027120144","https://openalex.org/W3036024602","https://openalex.org/W3039186706","https://openalex.org/W3047443805","https://openalex.org/W3048631361","https://openalex.org/W3097353710","https://openalex.org/W3102692100","https://openalex.org/W3102745911","https://openalex.org/W3103695279","https://openalex.org/W3109953061","https://openalex.org/W3111907788","https://openalex.org/W3112413862","https://openalex.org/W3123265576","https://openalex.org/W3124196789","https://openalex.org/W3128204528","https://openalex.org/W3136032573","https://openalex.org/W3204305289","https://openalex.org/W3214821343","https://openalex.org/W4282929851","https://openalex.org/W4292363360","https://openalex.org/W4387623802","https://openalex.org/W6602248423","https://openalex.org/W6838209734"],"related_works":["https://openalex.org/W2317401237","https://openalex.org/W1990800631","https://openalex.org/W2167120702","https://openalex.org/W2579567122","https://openalex.org/W2912065050","https://openalex.org/W2781510240","https://openalex.org/W2950186459","https://openalex.org/W2170114491","https://openalex.org/W2897298721","https://openalex.org/W2242624680"],"abstract_inverted_index":{"Reconstructing":[0],"a":[1,8,15,77,85,92,99,136,141,160,173,181],"high-resolution":[2],"hyperspectral":[3,11],"image":[4,12],"(HR-HSI)":[5],"by":[6,83,146,202],"using":[7,147],"single":[9,35,79,242],"low-resolution":[10],"(LR-HSI)":[13],"is":[14,185,200],"significant":[16],"technique":[17],"for":[18,177],"increasing":[19],"the":[20,27,31,47,53,73,127,153,165,188,194,232],"spatial":[21,195],"resolution":[22],"of":[23,30,49,143,207,234],"HSIs":[24,227,230],"and":[25,91,115,130,140,171,216,228,240],"overcoming":[26],"physical":[28],"limitation":[29],"HSI":[32,36,80,243],"sensor.":[33],"Most":[34],"super-resolution":[37,81,244],"methods":[38],"have":[39],"achieved":[40],"great":[41],"success":[42],"recently.":[43],"However,":[44],"owning":[45],"to":[46,64,150,163,191,236],"difficulty":[48],"acquiring":[50],"an":[51,108],"HSI,":[52],"available":[54],"training":[55,113,122,128,166],"samples":[56],"are":[57,133],"relatively":[58,65,120],"few,":[59],"which":[60,105],"will":[61],"inevitably":[62],"lead":[63],"low":[66],"performance.":[67],"To":[68,124],"address":[69],"this":[70],"issue,":[71],"in":[72],"paper,":[74],"we":[75,97,158],"propose":[76],"novel":[78],"method":[82,206],"combining":[84],"trainable":[86,100],"grouped":[87,101,161],"joint":[88,102,137,175],"tensor":[89,103,138,182],"dictionary":[90,139,176],"low-rank":[93,183],"prior":[94],"(GJTD-LR).":[95],"First,":[96],"design":[98],"dictionary,":[104],"can":[106],"build":[107],"accurate":[109],"mapping":[110],"relationship":[111],"between":[112],"HR-HSIs":[114],"their":[116],"corresponding":[117],"LR-HSIs":[118],"with":[119],"few":[121],"samples.":[123],"be":[125],"specific,":[126],"HR-HSI":[129],"LR-HSI":[131],"pairs":[132],"decomposed":[134],"into":[135,168,187],"set":[142],"sparse":[144],"coefficients":[145],"tensor-tensor":[148],"product":[149],"fully":[151],"preserve":[152],"spectral":[154],"correlation.":[155,196],"In":[156],"addition,":[157],"apply":[159],"strategy":[162],"divide":[164],"images":[167],"several":[169],"groups":[170],"learn":[172],"compact":[174],"each":[178],"group.":[179],"Second,":[180],"model":[184,190],"forced":[186],"reconstruction":[189],"further":[192],"capture":[193],"At":[197],"last,":[198],"GJTD-LR":[199,235],"optimized":[201],"employing":[203],"alternating":[204],"direction":[205],"multipliers":[208],"(ADMM),":[209],"soft":[210],"threshold":[211],"algorithm,":[212],"singular":[213],"value":[214],"decomposition":[215],"fourier":[217],"domain":[218],"transform.":[219],"The":[220],"experimental":[221],"results":[222],"on":[223],"both":[224],"remote":[225],"sensed":[226],"indoor":[229],"show":[231],"superiority":[233],"some":[237],"other":[238],"traditional":[239],"advanced":[241],"methods.":[245]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
