{"id":"https://openalex.org/W4387092373","doi":"https://doi.org/10.1109/tci.2023.3314969","title":"High Resolution LED-Based Snapshot Compressive Spectral Video Imaging With Deep Neural Networks","display_name":"High Resolution LED-Based Snapshot Compressive Spectral Video Imaging With Deep Neural Networks","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4387092373","doi":"https://doi.org/10.1109/tci.2023.3314969"},"language":"en","primary_location":{"id":"doi:10.1109/tci.2023.3314969","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tci.2023.3314969","pdf_url":null,"source":{"id":"https://openalex.org/S4210233665","display_name":"IEEE Transactions on Computational Imaging","issn_l":"2333-9403","issn":["2333-9403","2573-0436"],"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 Computational Imaging","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/A5101433933","display_name":"Xiao Ma","orcid":"https://orcid.org/0000-0002-8986-124X"},"institutions":[{"id":"https://openalex.org/I86501945","display_name":"University of Delaware","ror":"https://ror.org/01sbq1a82","country_code":"US","type":"education","lineage":["https://openalex.org/I86501945"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xiao Ma","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, USA"],"raw_orcid":"https://orcid.org/0000-0002-8986-124X","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, USA","institution_ids":["https://openalex.org/I86501945"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015431603","display_name":"Xin Yuan","orcid":"https://orcid.org/0000-0002-8311-7524"},"institutions":[{"id":"https://openalex.org/I3133055985","display_name":"Westlake University","ror":"https://ror.org/05hfa4n20","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133055985"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Yuan","raw_affiliation_strings":["School of Engineering, Westlake University, Hangzhou, Zhejiang, China"],"raw_orcid":"https://orcid.org/0000-0002-8311-7524","affiliations":[{"raw_affiliation_string":"School of Engineering, Westlake University, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I3133055985"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005357824","display_name":"Gonzalo R. Arce","orcid":"https://orcid.org/0000-0001-7163-7111"},"institutions":[{"id":"https://openalex.org/I86501945","display_name":"University of Delaware","ror":"https://ror.org/01sbq1a82","country_code":"US","type":"education","lineage":["https://openalex.org/I86501945"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gonzalo R. Arce","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, USA"],"raw_orcid":"https://orcid.org/0000-0001-7163-7111","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, USA","institution_ids":["https://openalex.org/I86501945"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101433933"],"corresponding_institution_ids":["https://openalex.org/I86501945"],"apc_list":null,"apc_paid":null,"fwci":0.8282,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.67719219,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"9","issue":null,"first_page":"869","last_page":"880"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/T11569","display_name":"Optical Coherence Tomography Applications","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/computer-science","display_name":"Computer science","score":0.7005113959312439},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.6710909008979797},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6459546089172363},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.5130668878555298},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5072545409202576},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.4997122287750244},{"id":"https://openalex.org/keywords/spectral-imaging","display_name":"Spectral imaging","score":0.49680188298225403},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45338499546051025},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4525130093097687},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.4471726417541504},{"id":"https://openalex.org/keywords/spectral-resolution","display_name":"Spectral resolution","score":0.4355162978172302},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43006718158721924},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35480737686157227},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34947723150253296},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1700606346130371},{"id":"https://openalex.org/keywords/spectral-line","display_name":"Spectral line","score":0.1542893648147583},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.11769691109657288},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09763947129249573}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7005113959312439},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.6710909008979797},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6459546089172363},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.5130668878555298},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5072545409202576},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.4997122287750244},{"id":"https://openalex.org/C3232514","wikidata":"https://www.wikidata.org/wiki/Q7575196","display_name":"Spectral imaging","level":2,"score":0.49680188298225403},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45338499546051025},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4525130093097687},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4471726417541504},{"id":"https://openalex.org/C124967146","wikidata":"https://www.wikidata.org/wiki/Q3457898","display_name":"Spectral resolution","level":3,"score":0.4355162978172302},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43006718158721924},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35480737686157227},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34947723150253296},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1700606346130371},{"id":"https://openalex.org/C4839761","wikidata":"https://www.wikidata.org/wiki/Q212111","display_name":"Spectral line","level":2,"score":0.1542893648147583},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.11769691109657288},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09763947129249573},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tci.2023.3314969","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tci.2023.3314969","pdf_url":null,"source":{"id":"https://openalex.org/S4210233665","display_name":"IEEE Transactions on Computational Imaging","issn_l":"2333-9403","issn":["2333-9403","2573-0436"],"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 Computational Imaging","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W1538237237","https://openalex.org/W1901129140","https://openalex.org/W1965134966","https://openalex.org/W1979377931","https://openalex.org/W1979449170","https://openalex.org/W1986213393","https://openalex.org/W1986701690","https://openalex.org/W2024449816","https://openalex.org/W2028339660","https://openalex.org/W2079319869","https://openalex.org/W2098279617","https://openalex.org/W2099473796","https://openalex.org/W2100109944","https://openalex.org/W2100556411","https://openalex.org/W2117008804","https://openalex.org/W2163753106","https://openalex.org/W2170608472","https://openalex.org/W2194775991","https://openalex.org/W2233161451","https://openalex.org/W2341031973","https://openalex.org/W2556661275","https://openalex.org/W2752782242","https://openalex.org/W2806340326","https://openalex.org/W2884144629","https://openalex.org/W2892288283","https://openalex.org/W2895667927","https://openalex.org/W2896421827","https://openalex.org/W2904994991","https://openalex.org/W2949258649","https://openalex.org/W2963764784","https://openalex.org/W2963782415","https://openalex.org/W2963825193","https://openalex.org/W2967656728","https://openalex.org/W2999482976","https://openalex.org/W3006196464","https://openalex.org/W3009338239","https://openalex.org/W3010785697","https://openalex.org/W3022689538","https://openalex.org/W3041490661","https://openalex.org/W3108567638","https://openalex.org/W3134510327","https://openalex.org/W3135630532","https://openalex.org/W3214178207","https://openalex.org/W4200001828","https://openalex.org/W4225682675","https://openalex.org/W4288045208","https://openalex.org/W6639824700","https://openalex.org/W6703405610"],"related_works":["https://openalex.org/W2896778670","https://openalex.org/W4312088035","https://openalex.org/W2537408670","https://openalex.org/W2765230616","https://openalex.org/W2005704400","https://openalex.org/W1965891716","https://openalex.org/W4323836127","https://openalex.org/W4230402950","https://openalex.org/W2017362239","https://openalex.org/W2765385239"],"abstract_inverted_index":{"Multi-spectral":[0],"and":[1,13,89,161,168,185],"hyper-spectral":[2,159],"imaging":[3],"usually":[4],"requires":[5],"scanning":[6,61],"in":[7,120,183],"the":[8,15,60,68,93,96,121,138,162,177,187],"spatial":[9],"or":[10],"spectral":[11,108,145,167],"dimensions":[12],"thus":[14],"temporal":[16,169],"resolution":[17,35],"is":[18,28,75,78,99],"often":[19],"compromised.":[20],"Although":[21],"various":[22],"methods":[23],"have":[24],"been":[25],"proposed,":[26,76],"it":[27],"still":[29],"challenging":[30],"to":[31,58,144,155],"capture":[32,156],"high":[33,157],"spatial-temporal-spectral":[34],"simultaneously.":[36],"In":[37,92],"this":[38],"paper,":[39],"we":[40],"propose":[41],"LeSTI++,":[42],"a":[43,71,81,102,106,133,148],"<italic":[44,51],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[45,52,201],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">deep":[46],"learning":[47],"reconstruction</i>":[48],"for":[49],"an":[50],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">LED-based":[53],"compressive":[54],"spectral-temporal":[55],"imaging</i>":[56],"system":[57],"overcome":[59],"limitation":[62],"of":[63,80,189],"previous":[64,173],"approaches.":[65],"After":[66],"describing":[67],"hardware":[69],"setup,":[70],"three-step":[72],"reconstruction":[73,163],"algorithm":[74,152,179],"which":[77,114],"composed":[79],"plug-and-play":[82],"(PnP)":[83],"algorithm,":[84],"motion":[85,130],"based":[86],"frame":[87],"interpolation,":[88],"spectra":[90],"converter.":[91,150],"first":[94],"step,":[95,123],"captured":[97],"measurement":[98],"decoded":[100],"by":[101,132,147],"PnP":[103],"framework":[104],"using":[105],"trained":[107,149],"video":[109],"(SpVi)":[110],"denoiser,":[111],"dubbed":[112],"PnP-SpVi,":[113],"specially":[115],"fits":[116],"our":[117],"data.":[118,193],"Then":[119],"second":[122],"missing":[124],"LED":[125,140],"images":[126,141,146],"are":[127,142],"interpolated":[128],"with":[129,180,191],"estimation":[131],"pre-trained":[134],"neural":[135],"network.":[136],"Lastly,":[137],"predicted":[139],"projected":[143],"This":[151],"allows":[153],"us":[154],"speed":[158],"targets":[160],"results":[164],"achieve":[165],"higher":[166],"resolutions":[170],"simultaneously":[171],"than":[172],"solutions.":[174],"We":[175],"compare":[176],"proposed":[178,188],"state-of-the-art":[181],"algorithms":[182],"simulation,":[184],"verify":[186],"LeSTI++":[190],"experimental":[192],"The":[194],"code":[195],"can":[196],"be":[197],"accessed":[198],"at":[199],"<uri":[200],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">https://github.com/dahaorendrm/SCI_2.0_python</uri>":[202],".":[203]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
