{"id":"https://openalex.org/W2766419681","doi":"https://doi.org/10.1109/iccvw.2017.63","title":"In Defense of Shallow Learned Spectral Reconstruction from RGB Images","display_name":"In Defense of Shallow Learned Spectral Reconstruction from RGB Images","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2766419681","doi":"https://doi.org/10.1109/iccvw.2017.63","mag":"2766419681"},"language":"en","primary_location":{"id":"doi:10.1109/iccvw.2017.63","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw.2017.63","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Computer Vision Workshops (ICCVW)","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/A5048076960","display_name":"Jiqing Wu","orcid":"https://orcid.org/0000-0002-6898-8698"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jiqing Wu","raw_affiliation_strings":["D-ITET ETH, Computer Vision Lab, Zurich"],"affiliations":[{"raw_affiliation_string":"D-ITET ETH, Computer Vision Lab, Zurich","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090368449","display_name":"Jonas Aeschbacher","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jonas Aeschbacher","raw_affiliation_strings":["D-ITET ETH, Computer Vision Lab, Zurich"],"affiliations":[{"raw_affiliation_string":"D-ITET ETH, Computer Vision Lab, Zurich","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052236177","display_name":"Radu Timofte","orcid":"https://orcid.org/0000-0002-1478-0402"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Radu Timofte","raw_affiliation_strings":["D-ITET ETH, Computer Vision Lab, Zurich"],"affiliations":[{"raw_affiliation_string":"D-ITET ETH, Computer Vision Lab, Zurich","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5048076960"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.6421,"has_fulltext":false,"cited_by_count":169,"citation_normalized_percentile":{"value":0.96963833,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"471","last_page":"479"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9995999932289124,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9995999932289124,"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/overfitting","display_name":"Overfitting","score":0.7052032947540283},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7032884359359741},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6765297651290894},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6707820296287537},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.5555028319358826},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5020639896392822},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4512861371040344},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.44535452127456665},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4034004509449005},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3741149604320526},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.20994862914085388}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7052032947540283},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7032884359359741},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6765297651290894},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6707820296287537},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.5555028319358826},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5020639896392822},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4512861371040344},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44535452127456665},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4034004509449005},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3741149604320526},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.20994862914085388}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccvw.2017.63","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw.2017.63","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Computer Vision Workshops (ICCVW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.699999988079071,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321652","display_name":"Eidgen\u00f6ssische Technische Hochschule Z\u00fcrich","ror":"https://ror.org/05a28rw58"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W8423413","https://openalex.org/W935139217","https://openalex.org/W1529476013","https://openalex.org/W1655654231","https://openalex.org/W1791560514","https://openalex.org/W1963970296","https://openalex.org/W1967765274","https://openalex.org/W1992554745","https://openalex.org/W2008847349","https://openalex.org/W2012946078","https://openalex.org/W2013482901","https://openalex.org/W2014403078","https://openalex.org/W2014712350","https://openalex.org/W2015258183","https://openalex.org/W2035999119","https://openalex.org/W2039134469","https://openalex.org/W2042814309","https://openalex.org/W2066015174","https://openalex.org/W2069300393","https://openalex.org/W2082590892","https://openalex.org/W2087080155","https://openalex.org/W2098227151","https://openalex.org/W2100109944","https://openalex.org/W2121058967","https://openalex.org/W2128659236","https://openalex.org/W2135619855","https://openalex.org/W2149471024","https://openalex.org/W2150081556","https://openalex.org/W2160547390","https://openalex.org/W2161815745","https://openalex.org/W2167096957","https://openalex.org/W2194775991","https://openalex.org/W2263468737","https://openalex.org/W2312997001","https://openalex.org/W2466285044","https://openalex.org/W2476548250","https://openalex.org/W2520430674","https://openalex.org/W2559597482","https://openalex.org/W2604824122","https://openalex.org/W2739757502","https://openalex.org/W2963446712","https://openalex.org/W4233367343","https://openalex.org/W6600366598","https://openalex.org/W6624640001","https://openalex.org/W6638194035","https://openalex.org/W6679470320","https://openalex.org/W6684682732","https://openalex.org/W6727116172","https://openalex.org/W6898672684"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4297676672","https://openalex.org/W4281702477","https://openalex.org/W4378510483","https://openalex.org/W2490526372","https://openalex.org/W2786764570","https://openalex.org/W4309960894"],"abstract_inverted_index":{"Very":[0],"recent":[1,25],"Galliani":[2,161],"et":[3,31,75],"al.":[4,32,76],"[13]":[5],"proposed":[6],"a":[7,10,66,132],"method":[8,28,69,95],"using":[9],"very":[11,92],"deep":[12,93],"CNN":[13,94,165],"architecture":[14],"for":[15,135],"learned":[16,41,139],"spectral":[17,42,140],"reconstruction":[18,43,141],"and":[19,50,102,104,111,123,154,171],"showed":[20],"large":[21],"improvements":[22],"over":[23,83],"the":[24,39,109,117,124,164],"sparse":[26],"coding":[27],"of":[29,73],"Arad":[30,49,84],"[6].":[33],"In":[34],"this":[35],"paper":[36],"we":[37],"defend":[38],"shallow":[40,68,130,147],"methods":[44],"by:":[45],"(i)":[46],"first,":[47],"reimplementing":[48],"showing":[51],"that":[52,80,108],"it":[53],"can":[54],"achieve":[55],"significantly":[56,156],"better":[57],"results":[58],"than":[59,160],"those":[60],"originally":[61],"reported;":[62],"(ii)":[63],"second,":[64],"introducing":[65],"novel":[67],"based":[70],"on":[71,96],"A+":[72,131,148],"Timofte":[74],"[33]":[77],"from":[78,128,142],"super-resolution":[79],"substantially":[81],"improves":[82],"and,":[85],"moreover,":[86],"provides":[87],"comparable":[88],"performance":[89,126],"to":[90,169,178],"Galliani's":[91],"three":[97],"standard":[98],"benchmarks":[99],"(ICVL,":[100],"CAVE,":[101],"NUS);":[103],"(iii)":[105],"finally,":[106],"arguing":[107],"train":[110,158],"runtime":[112],"efficiency":[113],"as":[114,116,151],"well":[115,150],"clear":[118],"relation":[119],"between":[120],"its":[121],"parameters":[122],"achieved":[125],"makes":[127],"our":[129,146],"strong":[133],"baseline":[134],"further":[136],"research":[137],"in":[138],"RGB":[143],"images.":[144],"Moreover,":[145],"(as":[149],"Arad)":[152],"requires":[153],"uses":[155],"smaller":[157],"data":[159],"(and":[162],"generally":[163],"approaches),":[166],"is":[167,172],"robust":[168],"overfitting":[170],"easily":[173],"deployable":[174],"by":[175],"fast":[176],"training":[177],"newer":[179],"cameras.":[180]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":21},{"year":2024,"cited_by_count":29},{"year":2023,"cited_by_count":18},{"year":2022,"cited_by_count":23},{"year":2021,"cited_by_count":25},{"year":2020,"cited_by_count":27},{"year":2019,"cited_by_count":12},{"year":2018,"cited_by_count":12}],"updated_date":"2026-01-10T23:39:48.068659","created_date":"2025-10-10T00:00:00"}