{"id":"https://openalex.org/W3036024602","doi":"https://doi.org/10.1109/tip.2020.3044214","title":"Hyperspectral Image Super-Resolution via Deep Progressive Zero-Centric Residual Learning","display_name":"Hyperspectral Image Super-Resolution via Deep Progressive Zero-Centric Residual Learning","publication_year":2020,"publication_date":"2020-12-17","ids":{"openalex":"https://openalex.org/W3036024602","doi":"https://doi.org/10.1109/tip.2020.3044214","mag":"3036024602","pmid":"https://pubmed.ncbi.nlm.nih.gov/33332269"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2020.3044214","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2020.3044214","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2006.10300","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zhiyu Zhu","orcid":"https://orcid.org/0000-0002-0726-4522"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Zhiyu Zhu","raw_affiliation_strings":["City University of Hong Kong, Hong Kong"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Junhui Hou","orcid":"https://orcid.org/0000-0003-3431-2021"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Junhui Hou","raw_affiliation_strings":["City University of Hong Kong, Hong Kong"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jie Chen","orcid":"https://orcid.org/0000-0001-8419-4620"},"institutions":[{"id":"https://openalex.org/I141568987","display_name":"Hong Kong Baptist University","ror":"https://ror.org/0145fw131","country_code":"HK","type":"education","lineage":["https://openalex.org/I141568987"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Jie Chen","raw_affiliation_strings":["Hong Kong Baptist University, Hong Kong"],"affiliations":[{"raw_affiliation_string":"Hong Kong Baptist University, Hong Kong","institution_ids":["https://openalex.org/I141568987"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Huanqiang Zeng","orcid":"https://orcid.org/0000-0002-2802-7745"},"institutions":[{"id":"https://openalex.org/I119045251","display_name":"Huaqiao University","ror":"https://ror.org/03frdh605","country_code":"CN","type":"education","lineage":["https://openalex.org/I119045251"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huanqiang Zeng","raw_affiliation_strings":["School of Information Science and Engineering, Huaqiao University, Xiamen, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Huaqiao University, Xiamen, China","institution_ids":["https://openalex.org/I119045251"]}]},{"author_position":"last","author":{"id":null,"display_name":"Jiantao Zhou","orcid":"https://orcid.org/0000-0002-6015-2618"},"institutions":[{"id":"https://openalex.org/I204512498","display_name":"University of Macau","ror":"https://ror.org/01r4q9n85","country_code":"MO","type":"education","lineage":["https://openalex.org/I204512498"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Jiantao Zhou","raw_affiliation_strings":["University of Macau, Macau"],"affiliations":[{"raw_affiliation_string":"University of Macau, Macau","institution_ids":["https://openalex.org/I204512498"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I168719708"],"apc_list":null,"apc_paid":null,"fwci":8.9844,"has_fulltext":false,"cited_by_count":85,"citation_normalized_percentile":{"value":0.97922088,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"30","issue":null,"first_page":"1423","last_page":"1438"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9639999866485596,"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":0.9639999866485596,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.0203000009059906,"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":0.005200000014156103,"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/residual","display_name":"Residual","score":0.8069999814033508},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.663100004196167},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5997999906539917},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.5870000123977661},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.5321000218391418},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4997999966144562},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4950999915599823},{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.4715000092983246},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.390500009059906}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.8069999814033508},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7770000100135803},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6696000099182129},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.663100004196167},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5997999906539917},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.5870000123977661},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.5321000218391418},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4997999966144562},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4950999915599823},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.490200012922287},{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.4715000092983246},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.390500009059906},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.3889999985694885},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3765000104904175},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3732999861240387},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.3562999963760376},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3239000141620636},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3050000071525574},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.29429998993873596},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.2858000099658966},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.27469998598098755},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2700999975204468},{"id":"https://openalex.org/C116580362","wikidata":"https://www.wikidata.org/wiki/Q3878457","display_name":"Spatial normalization","level":3,"score":0.2694999873638153},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C70710897","wikidata":"https://www.wikidata.org/wiki/Q680081","display_name":"Separable space","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tip.2020.3044214","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2020.3044214","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","raw_type":"journal-article"},{"id":"pmid:33332269","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33332269","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null},{"id":"pmh:oai:arXiv.org:2006.10300","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2006.10300","pdf_url":"https://arxiv.org/pdf/2006.10300","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2006.10300","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2006.10300","pdf_url":"https://arxiv.org/pdf/2006.10300","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3180918617","display_name":null,"funder_award_id":"9042820 (CityU 11219019)","funder_id":"https://openalex.org/F4320321592","funder_display_name":"Research Grants Council, University Grants Committee"},{"id":"https://openalex.org/G8550936926","display_name":null,"funder_award_id":"9048123 (CityU 21211518)","funder_id":"https://openalex.org/F4320321592","funder_display_name":"Research Grants Council, University Grants Committee"}],"funders":[{"id":"https://openalex.org/F4320321592","display_name":"Research Grants Council, University Grants Committee","ror":"https://ror.org/00djwmt25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":75,"referenced_works":["https://openalex.org/W1973234061","https://openalex.org/W1997628313","https://openalex.org/W1999331929","https://openalex.org/W2009104648","https://openalex.org/W2012946078","https://openalex.org/W2021046129","https://openalex.org/W2026129516","https://openalex.org/W2048964123","https://openalex.org/W2056346283","https://openalex.org/W2087263574","https://openalex.org/W2089232561","https://openalex.org/W2097259623","https://openalex.org/W2100109944","https://openalex.org/W2101365302","https://openalex.org/W2113464037","https://openalex.org/W2114066324","https://openalex.org/W2133488119","https://openalex.org/W2133665775","https://openalex.org/W2143696753","https://openalex.org/W2152254169","https://openalex.org/W2154761409","https://openalex.org/W2179019672","https://openalex.org/W2194775991","https://openalex.org/W2327302159","https://openalex.org/W2474462684","https://openalex.org/W2517851280","https://openalex.org/W2558072172","https://openalex.org/W2572303978","https://openalex.org/W2592312604","https://openalex.org/W2607041014","https://openalex.org/W2611008809","https://openalex.org/W2620546134","https://openalex.org/W2741137940","https://openalex.org/W2750085043","https://openalex.org/W2755992512","https://openalex.org/W2766101120","https://openalex.org/W2767522909","https://openalex.org/W2792111852","https://openalex.org/W2793272303","https://openalex.org/W2798016471","https://openalex.org/W2804787979","https://openalex.org/W2887202977","https://openalex.org/W2887210918","https://openalex.org/W2889107641","https://openalex.org/W2901534369","https://openalex.org/W2903472399","https://openalex.org/W2903622424","https://openalex.org/W2910457605","https://openalex.org/W2922420408","https://openalex.org/W2926823990","https://openalex.org/W2947295162","https://openalex.org/W2963284277","https://openalex.org/W2963372104","https://openalex.org/W2963446712","https://openalex.org/W2964140612","https://openalex.org/W2964251418","https://openalex.org/W2989355516","https://openalex.org/W2997011911","https://openalex.org/W3000775737","https://openalex.org/W3007002906","https://openalex.org/W3019893222","https://openalex.org/W3048794210","https://openalex.org/W3097353710","https://openalex.org/W6631190155","https://openalex.org/W6638667902","https://openalex.org/W6639102338","https://openalex.org/W6677307086","https://openalex.org/W6724804524","https://openalex.org/W6745560452","https://openalex.org/W6749954789","https://openalex.org/W6755419504","https://openalex.org/W6759638512","https://openalex.org/W6774822216","https://openalex.org/W6780645432","https://openalex.org/W6781337563"],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"explores":[2],"the":[3,28,34,39,83,97,101,109,128,137,159,167,171,208],"problem":[4,35,63],"of":[5,27,82,162,170,196],"hyperspectral":[6],"image":[7,22,43,104,139],"(HSI)":[8],"super-resolution":[9],"that":[10,184],"merges":[11],"a":[12,18,47,70,93,113,119,191],"low":[13],"resolution":[14,20,72],"HSI":[15],"(LR-HSI)":[16],"and":[17,30,65,73,141,179,201,217],"high":[19,71],"multispectral":[21],"(HR-MSI).":[23],"The":[24,222],"cross-modality":[25],"distribution":[26],"spatial":[29,80],"spectral":[31,87,98,132],"information":[32],"makes":[33],"challenging.":[36],"Inspired":[37],"by":[38,126],"classic":[40],"wavelet":[41],"decomposition-based":[42],"fusion,":[44],"we":[45,143,153],"propose":[46,154],"novel":[48],"lightweight":[49],"deep":[50],"neural":[51],"network-based":[52],"framework,":[53],"namely":[54],"progressive":[55,94],"zero-centric":[56,74],"residual":[57,75,103,138,172],"network":[58],"(PZRes-Net),":[59],"to":[60,118,165,190],"address":[61],"this":[62],"efficiently":[64,140],"effectively.":[66],"Specifically,":[67],"PZRes-Net":[68,186,206],"learns":[69],"image,":[76],"which":[77,122],"contains":[78],"high-frequency":[79],"details":[81],"scene":[84],"across":[85,130],"all":[86,131],"bands,":[88],"from":[89],"both":[90,177,197],"inputs":[91],"in":[92,112,194],"fashion":[95],"along":[96],"dimension.":[99],"And":[100],"resulting":[102],"is":[105,123,224],"then":[106],"superimposed":[107],"onto":[108],"up-sampled":[110],"LR-HSI":[111],"mean-value":[114],"invariant":[115],"manner,":[116],"leading":[117],"coarse":[120],"HR-HSI,":[121],"further":[124],"refined":[125],"exploring":[127],"coherence":[129],"bands":[133],"simultaneously.":[134],"To":[135],"learn":[136],"effectively,":[142],"employ":[144],"spectral-spatial":[145],"separable":[146],"convolution":[147],"with":[148],"dense":[149],"connections.":[150],"In":[151],"addition,":[152],"zero-mean":[155,168],"normalization":[156],"implemented":[157],"on":[158],"feature":[160],"maps":[161],"each":[163],"layer":[164],"realize":[166],"characteristic":[169],"image.":[173],"Extensive":[174],"experiments":[175],"over":[176],"real":[178],"synthetic":[180],"benchmark":[181],"datasets":[182],"demonstrate":[183],"our":[185,205],"outperforms":[187],"state-of-the-art":[188],"methods":[189],"significant":[192],"extent":[193],"terms":[195],"4":[198],"quantitative":[199],"metrics":[200],"visual":[202],"quality,":[203],"e.g.,":[204],"improves":[207],"PSNR":[209],"more":[210],"than":[211],"3dB,":[212],"while":[213],"saving":[214],"2.3\u00d7":[215],"parameters":[216],"consuming":[218],"15\u00d7":[219],"less":[220],"FLOPs.":[221],"code":[223],"publicly":[225],"available":[226],"at":[227],"https://github.com/zbzhzhy/PZRes-Net.":[228]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":22},{"year":2023,"cited_by_count":26},{"year":2022,"cited_by_count":15},{"year":2021,"cited_by_count":6}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2020-06-25T00:00:00"}