{"id":"https://openalex.org/W4308233883","doi":"https://doi.org/10.1109/icip46576.2022.9897424","title":"Unsupervised Generative Network for Blind Hyperspectral Image Super-Resolution","display_name":"Unsupervised Generative Network for Blind Hyperspectral Image Super-Resolution","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4308233883","doi":"https://doi.org/10.1109/icip46576.2022.9897424"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9897424","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897424","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100462368","display_name":"Zhe Liu","orcid":"https://orcid.org/0000-0002-3756-2678"},"institutions":[{"id":"https://openalex.org/I173915773","display_name":"Yamaguchi University","ror":"https://ror.org/03cxys317","country_code":"JP","type":"education","lineage":["https://openalex.org/I173915773"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Zhe Liu","raw_affiliation_strings":["Yamaguchi University,Yamaguchi,Japan,753-8511"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yamaguchi University,Yamaguchi,Japan,753-8511","institution_ids":["https://openalex.org/I173915773"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086851360","display_name":"Xian\u2010Hua Han","orcid":"https://orcid.org/0000-0002-5003-3180"},"institutions":[{"id":"https://openalex.org/I173915773","display_name":"Yamaguchi University","ror":"https://ror.org/03cxys317","country_code":"JP","type":"education","lineage":["https://openalex.org/I173915773"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Xian-Hua Han","raw_affiliation_strings":["Yamaguchi University,Yamaguchi,Japan,753-8511"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yamaguchi University,Yamaguchi,Japan,753-8511","institution_ids":["https://openalex.org/I173915773"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080244427","display_name":"Jiande Sun","orcid":"https://orcid.org/0000-0001-6157-2051"},"institutions":[{"id":"https://openalex.org/I28006308","display_name":"Shandong Normal University","ror":"https://ror.org/01wy3h363","country_code":"CN","type":"education","lineage":["https://openalex.org/I28006308"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiande Sun","raw_affiliation_strings":["Shandong Normal University,China","Shandong Normal University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Normal University,China","institution_ids":["https://openalex.org/I28006308"]},{"raw_affiliation_string":"Shandong Normal University, China","institution_ids":["https://openalex.org/I28006308"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044216245","display_name":"Yen\u2010Wei Chen","orcid":"https://orcid.org/0000-0002-5952-0188"},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yen-Wei Chen","raw_affiliation_strings":["Ritsumeikan University,Japan","Ritsumeikan University, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ritsumeikan University,Japan","institution_ids":["https://openalex.org/I135768898"]},{"raw_affiliation_string":"Ritsumeikan University, Japan","institution_ids":["https://openalex.org/I135768898"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2621","last_page":"2625"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":1.0,"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"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9991000294685364,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9988999962806702,"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":0.8588732481002808},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.804550051689148},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7013508081436157},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6726664900779724},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.6103150844573975},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5590079426765442},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.540691077709198},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5284979939460754},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5092469453811646},{"id":"https://openalex.org/keywords/superresolution","display_name":"Superresolution","score":0.5001530647277832},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.424483060836792},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07331067323684692}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8588732481002808},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.804550051689148},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7013508081436157},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6726664900779724},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.6103150844573975},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5590079426765442},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.540691077709198},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5284979939460754},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5092469453811646},{"id":"https://openalex.org/C141239990","wikidata":"https://www.wikidata.org/wiki/Q957423","display_name":"Superresolution","level":3,"score":0.5001530647277832},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.424483060836792},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07331067323684692},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip46576.2022.9897424","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897424","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321408","display_name":"Ministry of Education","ror":"https://ror.org/01p262204"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2012946078","https://openalex.org/W2014775865","https://openalex.org/W2100109944","https://openalex.org/W2119681632","https://openalex.org/W2584763393","https://openalex.org/W2792111852","https://openalex.org/W2793521392","https://openalex.org/W2803552875","https://openalex.org/W2887210918","https://openalex.org/W2892302943","https://openalex.org/W2915063743","https://openalex.org/W2963284277","https://openalex.org/W2964140612","https://openalex.org/W2993041110","https://openalex.org/W2993046696","https://openalex.org/W2993917295","https://openalex.org/W3034400067","https://openalex.org/W3107490592","https://openalex.org/W3114279824","https://openalex.org/W3192509445","https://openalex.org/W6758724265"],"related_works":["https://openalex.org/W2901421464","https://openalex.org/W2932657337","https://openalex.org/W2317401237","https://openalex.org/W1990800631","https://openalex.org/W2167120702","https://openalex.org/W2579567122","https://openalex.org/W3102673927","https://openalex.org/W2327954668","https://openalex.org/W3004000015","https://openalex.org/W2559771220"],"abstract_inverted_index":{"Hyperspectral":[0],"(HS)":[1],"imaging":[2,96],"sacrifices":[3],"spatial":[4,20,124],"resolution":[5,11],"to":[6,40,62,89,129],"ensure":[7],"a":[8,56,77,85,101],"high":[9],"spectral":[10,16,126],"when":[12],"capturing":[13],"the":[14,23,91,112,118,123,132,146,167],"detailed":[15],"signature":[17],"at":[18],"each":[19],"location":[21],"of":[22,80,148],"scene.":[24],"To":[25,144],"compensate":[26],"for":[27,107,136],"this":[28],"deficiency,":[29],"fusing":[30],"low-resolution":[31],"HS":[32,43,158],"(LR-HS)":[33],"images":[34,39,45],"with":[35,166],"high-resolution":[36,42],"RGB":[37],"(HR-RGB)":[38],"obtain":[41],"(HR-HS)":[44],"has":[46],"attracted":[47],"remarkable":[48],"attention.":[49],"Recently,":[50],"deep":[51],"learning-based":[52],"fusion":[53],"methods":[54,74],"in":[55,66],"fully-supervised":[57],"manner":[58],"have":[59],"been":[60],"proven":[61],"make":[63],"great":[64],"progress":[65],"hyperspectral":[67],"image":[68,133,159],"super-resolution":[69],"(HSI-SR)":[70],"tasks.":[71],"However,":[72],"these":[73],"require":[75],"collecting":[76],"large":[78],"number":[79],"training":[81],"samples":[82],"and":[83,121,125,161,169],"constructing":[84,137],"non-blind":[86],"prediction":[87],"model":[88],"super-resolve":[90],"observations":[92],"captured":[93],"under":[94],"controlled":[95],"conditions.":[97],"This":[98],"study":[99],"proposes":[100],"novel":[102],"unsupervised":[103,170],"generative":[104],"network":[105,109],"(UGN)":[106],"learning":[108],"parameters":[110],"using":[111],"observed":[113],"LR-HS,":[114],"HR-RGB":[115],"only":[116],"without":[117],"corresponding":[119],"ground-truth,":[120],"designs":[122],"degradation":[127,134],"blocks":[128],"automatically":[130],"learn":[131],"operations":[135],"an":[138],"end-to-end":[139],"blind":[140],"HSI":[141],"SR":[142],"framework.":[143],"verify":[145],"effectiveness":[147],"our":[149],"proposed":[150],"method,":[151],"we":[152],"con-duct":[153],"experiments":[154],"on":[155],"two":[156],"benchmark":[157],"datasets":[160],"demonstrate":[162],"superior":[163],"performance":[164],"compared":[165],"super-vised":[168],"blind/non-blind":[171],"SoTA":[172],"methods.":[173]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}