{"id":"https://openalex.org/W3197931262","doi":"https://doi.org/10.1145/3461353.3461393","title":"Deep Multi-scale Recursive Residual Attention Network for Spectral Super Resolution","display_name":"Deep Multi-scale Recursive Residual Attention Network for Spectral Super Resolution","publication_year":2021,"publication_date":"2021-03-05","ids":{"openalex":"https://openalex.org/W3197931262","doi":"https://doi.org/10.1145/3461353.3461393","mag":"3197931262"},"language":"en","primary_location":{"id":"doi:10.1145/3461353.3461393","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3461353.3461393","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 the 5th International Conference on Innovation in Artificial Intelligence","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/A5049617591","display_name":"Shaolei Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210130660","display_name":"Xi'an High Tech University","ror":"https://ror.org/03vt7za95","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210130660"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaolei Zhang","raw_affiliation_strings":["Xi'an High-tech Research Institution, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi'an High-tech Research Institution, China","institution_ids":["https://openalex.org/I4210130660"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101990231","display_name":"Guangyuan Fu","orcid":"https://orcid.org/0000-0003-3393-5884"},"institutions":[{"id":"https://openalex.org/I4210130660","display_name":"Xi'an High Tech University","ror":"https://ror.org/03vt7za95","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210130660"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangyuan Fu","raw_affiliation_strings":["Xi'an High-tech Research Institution, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi'an High-tech Research Institution, China","institution_ids":["https://openalex.org/I4210130660"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016985407","display_name":"Hongqiao Wang","orcid":"https://orcid.org/0000-0003-1887-0891"},"institutions":[{"id":"https://openalex.org/I4210130660","display_name":"Xi'an High Tech University","ror":"https://ror.org/03vt7za95","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210130660"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongqiao Wang","raw_affiliation_strings":["Xi'an High-tech Research Institution, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi'an High-tech Research Institution, China","institution_ids":["https://openalex.org/I4210130660"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100697412","display_name":"Yuqing Zhao","orcid":"https://orcid.org/0000-0003-0091-1624"},"institutions":[{"id":"https://openalex.org/I4210130660","display_name":"Xi'an High Tech University","ror":"https://ror.org/03vt7za95","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210130660"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuqing Zhao","raw_affiliation_strings":["Xi'an High-tech Research Institution, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi'an High-tech Research Institution, China","institution_ids":["https://openalex.org/I4210130660"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210130660"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"26","last_page":"31"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9998000264167786,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9994999766349792,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8364709615707397},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.8301888704299927},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7940452098846436},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6630244851112366},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.602005124092102},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5365892052650452},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5290616750717163},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5232499837875366},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46087056398391724},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36678001284599304},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3233419358730316},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.29894107580184937},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10621029138565063}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8364709615707397},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.8301888704299927},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7940452098846436},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6630244851112366},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.602005124092102},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5365892052650452},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5290616750717163},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5232499837875366},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46087056398391724},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36678001284599304},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3233419358730316},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29894107580184937},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10621029138565063},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3461353.3461393","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3461353.3461393","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 the 5th International Conference on Innovation in Artificial Intelligence","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":12,"referenced_works":["https://openalex.org/W8423413","https://openalex.org/W2012946078","https://openalex.org/W2100109944","https://openalex.org/W2520430674","https://openalex.org/W2747898905","https://openalex.org/W2752782242","https://openalex.org/W2848020186","https://openalex.org/W2866634454","https://openalex.org/W2893739000","https://openalex.org/W2963704386","https://openalex.org/W3035556176","https://openalex.org/W3035598238"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W4312814274","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W2072166414","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312"],"abstract_inverted_index":{"The":[0],"Spectral":[1],"super-resolution":[2],"methods":[3,148],"based":[4,70],"on":[5,71,130],"deep":[6,72],"learning":[7,94],"have":[8],"achieved":[9],"promising":[10],"performance":[11],"when":[12,149],"the":[13,43,59,92,97],"network":[14,60,73,90,103],"models":[15],"are":[16],"well":[17],"trained,":[18],"most":[19],"of":[20,102],"which":[21,53,79],"require":[22],"sufficient":[23],"training":[24,45,153],"dataset.":[25,154],"However,":[26],"capturing":[27],"enough":[28],"paired":[29],"color":[30],"image":[31],"and":[32,99,124],"hyperspectral":[33,132],"images":[34],"is":[35,47,54,74,80,115,151],"expensive":[36],"or":[37],"complex":[38],"under":[39],"existing":[40],"conditions.":[41],"Thus,":[42],"available":[44],"dataset":[46],"still":[48],"small":[49,152],"to":[50,56,104,117],"some":[51],"extent,":[52],"prone":[55],"over-fitting":[57],"as":[58,82],"becomes":[61],"deeper.":[62],"To":[63],"solve":[64],"this":[65],"problem,":[66],"a":[67],"novel":[68],"method":[69,137],"proposed":[75,116],"for":[76],"spectral":[77,146],"super-resolution,":[78],"named":[81],"multi-scale":[83,111],"recursive":[84,93],"residual":[85,112],"attention":[86,113],"network.":[87],"Without":[88],"increasing":[89],"parameters,":[91],"can":[95,138],"increase":[96],"depth":[98],"receptive":[100],"field":[101],"strengthen":[105],"nonlinear":[106],"modeling":[107],"capabilities.":[108],"In":[109],"addition,":[110],"block":[114],"exploit":[118],"contextual":[119],"information":[120],"in":[121],"different":[122],"scale":[123],"adjust":[125],"channel-wise":[126],"features":[127],"adaptively.":[128],"Experiments":[129],"public":[131],"datasets":[133],"demonstrates":[134],"that":[135],"our":[136],"achieve":[139],"better":[140],"reconstruction":[141,147],"effects":[142],"compared":[143],"with":[144],"other":[145],"there":[150]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}