{"id":"https://openalex.org/W4308235911","doi":"https://doi.org/10.1109/icip46576.2022.9897926","title":"Hyperspectral Reconstruction Using Auxiliary Rgb Learning From A Snapshot Image","display_name":"Hyperspectral Reconstruction Using Auxiliary Rgb Learning From A Snapshot Image","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4308235911","doi":"https://doi.org/10.1109/icip46576.2022.9897926"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9897926","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897926","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/A5014430005","display_name":"Kazuhiro Yamawaki","orcid":"https://orcid.org/0000-0003-4670-548X"},"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":"Kazuhiro Yamawaki","raw_affiliation_strings":["Yamaguchi University,Graduate School of Science and Technology for Innovation,Japan,753-8511"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yamaguchi University,Graduate School of Science and Technology for Innovation,Japan,753-8511","institution_ids":["https://openalex.org/I173915773"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006287654","display_name":"Kouhei Yorimoto","orcid":null},"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":"Kouhei Yorimoto","raw_affiliation_strings":["Yamaguchi University,Graduate School of Science and Technology for Innovation,Japan,753-8511"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yamaguchi University,Graduate School of Science and Technology for Innovation,Japan,753-8511","institution_ids":["https://openalex.org/I173915773"]}]},{"author_position":"last","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,Graduate School of Science and Technology for Innovation,Japan,753-8511"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yamaguchi University,Graduate School of Science and Technology for Innovation,Japan,753-8511","institution_ids":["https://openalex.org/I173915773"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I173915773"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"186","last_page":"190"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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/T10977","display_name":"Optical Imaging and Spectroscopy Techniques","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9988999962806702,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7895932197570801},{"id":"https://openalex.org/keywords/snapshot","display_name":"Snapshot (computer storage)","score":0.7753818035125732},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6930575966835022},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6162775158882141},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6104552745819092},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.5795623660087585},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5612044930458069},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.5460304617881775},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5387078523635864},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.4136888086795807},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40948736667633057}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7895932197570801},{"id":"https://openalex.org/C55282118","wikidata":"https://www.wikidata.org/wiki/Q252683","display_name":"Snapshot (computer storage)","level":2,"score":0.7753818035125732},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6930575966835022},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6162775158882141},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6104552745819092},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.5795623660087585},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5612044930458069},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.5460304617881775},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5387078523635864},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.4136888086795807},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40948736667633057},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip46576.2022.9897926","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897926","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":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1975622988","https://openalex.org/W1988386267","https://openalex.org/W2010797000","https://openalex.org/W2028349405","https://openalex.org/W2079319869","https://openalex.org/W2084591647","https://openalex.org/W2097273723","https://openalex.org/W2154761409","https://openalex.org/W2169832615","https://openalex.org/W2170608472","https://openalex.org/W2462946880","https://openalex.org/W2511401065","https://openalex.org/W2533971697","https://openalex.org/W2766101120","https://openalex.org/W2770113520","https://openalex.org/W2804823968","https://openalex.org/W2903472399","https://openalex.org/W2963764784","https://openalex.org/W2964984565","https://openalex.org/W2983736948","https://openalex.org/W2992316425","https://openalex.org/W4233367343","https://openalex.org/W4233489315","https://openalex.org/W6752178629"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2070598848","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2060875994","https://openalex.org/W2027399350","https://openalex.org/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W2019190440"],"abstract_inverted_index":{"To":[0,104],"solve":[1],"the":[2,10,23,39,55,60,69,77,80,89,97,102,117,122,130,134,139,144,151,172,179,183,188,194,205,213,218,221],"low":[3],"spatial":[4],"and":[5,32,88,128,171,176],"temporal":[6],"resolution":[7],"issue":[8],"in":[9,47,125,182],"conventional":[11],"hyperspectral":[12],"(HS)":[13],"imaging":[14],"sensors,":[15],"coded":[16],"aperture":[17],"snapshot":[18,31,67,123],"HS":[19,25,41,57,81,146,174,189],"imaging,":[20],"which":[21],"encodes":[22],"3D":[24],"image":[26,58,82,120,124,167],"into":[27],"a":[28,85,162],"2D":[29],"compressive":[30,66],"then":[33,177],"adopts":[34],"computational":[35],"technique":[36],"to":[37,53,76,115,137,187],"recover":[38],"latent":[40,56,145],"image,":[42,175],"has":[43],"attracted":[44],"remarkable":[45],"attention":[46],"recent":[48],"year.":[49],"This":[50],"study":[51],"aims":[52],"reconstruct":[54,116],"with":[59,96,154,204,212],"detail":[61],"spectral":[62,90,166,195],"distribution":[63],"from":[64,121],"its":[65],"using":[68],"deep":[70],"convolution":[71],"neural":[72],"network":[73],"(DCNN).":[74],"Due":[75],"ill-posed":[78],"nature,":[79],"reconstruction":[83,142,159,190],"is":[84,92],"challenge":[86],"task,":[87],"distortion":[91],"unavoidably":[93],"produced":[94],"even":[95],"powerful":[98],"learning":[99,113,196,207],"capability":[100],"of":[101,133,143,161,165,220],"DCNN.":[103],"alleviate":[105],"this":[106],"limitation,":[107],"we":[108,149],"leverage":[109],"an":[110],"auxiliary":[111,135,184,206],"RGB":[112,119,185],"task":[114,136],"corresponding":[118],"training":[126],"phase,":[127],"incorporate":[129],"learned":[131],"features":[132,181],"assist":[138],"more":[140],"difficult":[141],"image.":[147],"Specifically,":[148],"design":[150],"DCNN":[152],"architecture":[153],"two":[155],"branches":[156],"for":[157,192],"both":[158],"learnings":[160],"small":[163],"number":[164],"(such":[168],"as":[169],"RGB)":[170],"full-spectral":[173],"integrate":[178],"intermediate":[180],"branch":[186,191],"augment":[193],"capability.":[197],"Experimental":[198],"results":[199],"demonstrate":[200],"our":[201],"proposed":[202],"method":[203],"can":[208],"achieve":[209],"comparable":[210],"performance":[211],"state-of-the-art":[214],"methods":[215],"while":[216],"enable":[217],"reduction":[219],"model":[222],"size.":[223]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
