{"id":"https://openalex.org/W3162484869","doi":"https://doi.org/10.1109/icpr48806.2021.9412321","title":"Deep Residual Attention Network for Hyperspectral Image Reconstruction","display_name":"Deep Residual Attention Network for Hyperspectral Image Reconstruction","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3162484869","doi":"https://doi.org/10.1109/icpr48806.2021.9412321","mag":"3162484869"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9412321","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412321","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-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/A5008920719","display_name":"Yorimoto Kohei","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":true,"raw_author_name":"Yorimoto Kohei","raw_affiliation_strings":["Graduate School of Science and Technology for Innovation, Yamaguchi University, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Technology for Innovation, Yamaguchi University, Japan","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":["Graduate School of Science and Technology for Innovation, Yamaguchi University, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Technology for Innovation, Yamaguchi University, Japan","institution_ids":["https://openalex.org/I173915773"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5008920719"],"corresponding_institution_ids":["https://openalex.org/I173915773"],"apc_list":null,"apc_paid":null,"fwci":1.2259,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.75238311,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"8547","last_page":"8553"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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/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/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9994999766349792,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.8119450807571411},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7933351397514343},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7303672432899475},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6960176229476929},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6802346706390381},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.6585532426834106},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5825446248054504},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5393248796463013},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5163413286209106},{"id":"https://openalex.org/keywords/snapshot","display_name":"Snapshot (computer storage)","score":0.5064572095870972},{"id":"https://openalex.org/keywords/inverse-problem","display_name":"Inverse problem","score":0.4787154197692871},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.4409438669681549},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.42033421993255615},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.3211466073989868},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.22820237278938293},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16971245408058167},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.151082843542099}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8119450807571411},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7933351397514343},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7303672432899475},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6960176229476929},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6802346706390381},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.6585532426834106},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5825446248054504},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5393248796463013},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5163413286209106},{"id":"https://openalex.org/C55282118","wikidata":"https://www.wikidata.org/wiki/Q252683","display_name":"Snapshot (computer storage)","level":2,"score":0.5064572095870972},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.4787154197692871},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.4409438669681549},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.42033421993255615},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3211466073989868},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.22820237278938293},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16971245408058167},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.151082843542099},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr48806.2021.9412321","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412321","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.7799999713897705,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1970762771","https://openalex.org/W1975622988","https://openalex.org/W1984864470","https://openalex.org/W2012946078","https://openalex.org/W2065579444","https://openalex.org/W2084591647","https://openalex.org/W2097273723","https://openalex.org/W2109149397","https://openalex.org/W2133665775","https://openalex.org/W2154761409","https://openalex.org/W2169832615","https://openalex.org/W2462946880","https://openalex.org/W2520430674","https://openalex.org/W2533971697","https://openalex.org/W2606306511","https://openalex.org/W2766101120","https://openalex.org/W2770113520","https://openalex.org/W2903251150","https://openalex.org/W2903472399","https://openalex.org/W2963372104","https://openalex.org/W2963764784","https://openalex.org/W2964984565","https://openalex.org/W2983736948","https://openalex.org/W6757063735","https://openalex.org/W6766641779"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2070598848","https://openalex.org/W2060875994","https://openalex.org/W2027399350","https://openalex.org/W1928301487","https://openalex.org/W2151220638","https://openalex.org/W2725829804"],"abstract_inverted_index":{"Coded":[0],"aperture":[1],"snapshot":[2,29,51],"spectral":[3,10,130,141,169],"imaging":[4],"(CASSI)":[5],"captures":[6],"a":[7,13,35,50,101,114],"full":[8],"frame":[9],"image":[11,16,25,59,78,154],"as":[12,30],"single":[14],"compressive":[15],"and":[17,68,105,129,140,168,190],"is":[18,34],"mandatory":[19],"to":[20,40,144],"reconstruct":[21],"the":[22,28,31,57,64,83,108,126,133,137,147],"underlying":[23,134],"hyperspectral":[24],"(HSI)":[26],"from":[27,49],"post-processing,":[32],"which":[33],"challenge":[36],"inverse":[37,91],"problem":[38],"due":[39],"its":[41],"ill-posed":[42],"nature.":[43],"Existing":[44],"methods":[45,185],"for":[46,55,75,89,123,151],"HSI":[47,96,135,153,177],"reconstruction":[48,73,97,109],"usually":[52],"employs":[53],"optimization":[54],"solving":[56],"formulated":[58],"degradation":[60],"model":[61],"regularized":[62],"with":[63,136],"empirically":[65],"designed":[66],"priors,":[67],"still":[69],"cannot":[70],"achieve":[71],"enough":[72],"accuracy":[74],"real":[76],"HS":[77],"analysis":[79],"systems.":[80],"Motivated":[81],"by":[82],"recent":[84],"advances":[85],"of":[86,103],"deep":[87,93,116],"learning":[88,94,125],"different":[90],"problems,":[92],"based":[95,121],"method":[98,182],"has":[99],"attracted":[100],"lot":[102],"attention":[104,159,170],"can":[106],"boost":[107],"performance.":[110],"This":[111],"study":[112],"proposes":[113],"novel":[115],"convolutional":[117],"neural":[118],"network":[119],"(DCNN)":[120],"framework":[122],"effectively":[124],"spatial":[127,139,167],"structure":[128],"attribute":[131],"in":[132,186],"reciprocal":[138],"modules.":[142],"Further,":[143],"adaptively":[145],"leverage":[146],"useful":[148],"learned":[149],"feature":[150],"better":[152],"reconstruction,":[155],"we":[156],"integrate":[157],"residual":[158],"modules":[160],"into":[161],"our":[162,181],"DCNN":[163],"via":[164],"exploring":[165],"both":[166,187],"maps.":[171],"Experimental":[172],"results":[173],"on":[174],"two":[175],"benchmark":[176],"datasets":[178],"show":[179],"that":[180],"outperforms":[183],"state-of-the-art":[184],"quantitative":[188],"values":[189],"visual":[191],"effects.":[192]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}