{"id":"https://openalex.org/W2607041014","doi":"https://doi.org/10.1109/cvpr.2017.618","title":"Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution","display_name":"Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2607041014","doi":"https://doi.org/10.1109/cvpr.2017.618","mag":"2607041014"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2017.618","doi":"https://doi.org/10.1109/cvpr.2017.618","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2017.618","pdf_url":null,"source":{"id":"https://openalex.org/S4210176548","display_name":"IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings","issn_l":"1063-6919","issn":["1063-6919","2332-564X","2575-7075"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},"parent_works":[],"lineage":["https://openalex.org/W2607041014"],"child_works_count":0,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1704.03915","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020775577","display_name":"Wei\u2010Sheng Lai","orcid":"https://orcid.org/0000-0001-5753-6445","observed_orcids":["https://orcid.org/0000-0001-5753-6445"]},"institutions":[{"id":"https://openalex.org/I156087764","display_name":"University of California, Merced","ror":"https://ror.org/00d9ah105","country_code":"US","type":"education","lineage":["https://openalex.org/I156087764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei-Sheng Lai","raw_affiliation_strings":["University of California, Merced"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Merced","institution_ids":["https://openalex.org/I156087764"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101766030","display_name":"Jia\u2010Bin Huang","orcid":"https://orcid.org/0000-0002-0536-3658","observed_orcids":["https://orcid.org/0000-0002-0536-3658"]},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jia-Bin Huang","raw_affiliation_strings":["Virginia Tech"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Virginia Tech","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108521995","display_name":"Narendra Ahuja","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Narendra Ahuja","raw_affiliation_strings":["University of Illinois, Urbana-Champaign"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois, Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100418319","display_name":"Ming\u2013Hsuan Yang","orcid":"https://orcid.org/0000-0003-4848-2304","observed_orcids":["https://orcid.org/0000-0003-4848-2304","https://orcid.org/0000-0001-6364-0417"]},"institutions":[{"id":"https://openalex.org/I156087764","display_name":"University of California, Merced","ror":"https://ror.org/00d9ah105","country_code":"US","type":"education","lineage":["https://openalex.org/I156087764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ming-Hsuan Yang","raw_affiliation_strings":["University of California, Merced"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Merced","institution_ids":["https://openalex.org/I156087764"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":69.4212,"has_fulltext":true,"cited_by_count":3145,"citation_normalized_percentile":{"value":0.99879227,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"5835","last_page":"5843"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","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"}},"topics":[{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","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"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.0,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0,"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/single-image-super-resolution","display_name":"single image super-resolution","score":0.9660000205039978},{"id":"https://openalex.org/keywords/laplacian-pyramid","display_name":"Laplacian pyramid","score":0.9459999799728394},{"id":"https://openalex.org/keywords/progressive-reconstruction","display_name":"progressive reconstruction","score":0.6029999852180481},{"id":"https://openalex.org/keywords/transposed-convolution","display_name":"transposed convolution","score":0.7860000133514404},{"id":"https://openalex.org/keywords/deep-supervision","display_name":"deep supervision","score":0.8579999804496765},{"id":"https://openalex.org/keywords/charbonnier-loss","display_name":"Charbonnier loss","score":0.9959999918937683},{"id":"https://openalex.org/keywords/multi-scale-forecasting","display_name":"multi-scale prediction","score":0.8619999885559082},{"id":"https://openalex.org/keywords/computational-efficiency","display_name":"computational efficiency","score":0.5960000157356262}],"concepts":[{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.9002092480659485},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7615216970443726},{"id":"https://openalex.org/C49608258","wikidata":"https://www.wikidata.org/wiki/Q611705","display_name":"Bicubic interpolation","level":4,"score":0.6553388237953186},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6548091173171997},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6416905522346497},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.6404483318328857},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.5455136299133301},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5256156325340271},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4967342019081116},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.462639719247818},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.45429980754852295},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44922417402267456},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43752050399780273},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.4350469708442688},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.396235853433609},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3896787762641907},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3223951458930969},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2867741584777832},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1720893383026123},{"id":"https://openalex.org/C171836373","wikidata":"https://www.wikidata.org/wiki/Q2266329","display_name":"Linear interpolation","level":3,"score":0.17189499735832214},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cvpr.2017.618","doi":"https://doi.org/10.1109/cvpr.2017.618","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2017.618","pdf_url":null,"source":{"id":"https://openalex.org/S4210176548","display_name":"IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings","issn_l":"1063-6919","issn":["1063-6919","2332-564X","2575-7075"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1704.03915","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1704.03915","pdf_url":"https://arxiv.org/pdf/1704.03915","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.1704.03915","doi":"https://doi.org/10.48550/arxiv.1704.03915","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1704.03915","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1704.03915","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1704.03915","pdf_url":"https://arxiv.org/pdf/1704.03915","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G336420141","display_name":null,"funder_award_id":"N00014-16-1-2314","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G4613586837","display_name":"CAREER: Visual Tracking with Online and Prior Learning","funder_award_id":"1149783","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6671297155","display_name":null,"funder_award_id":"CAREER","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8876996369","display_name":null,"funder_award_id":"N00014","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2607041014.pdf","grobid_xml":"https://content.openalex.org/works/W2607041014.grobid-xml"},"referenced_works_count":46,"referenced_works":["https://openalex.org/W7682646","https://openalex.org/W648143168","https://openalex.org/W845365781","https://openalex.org/W935139217","https://openalex.org/W968293391","https://openalex.org/W1677182931","https://openalex.org/W1791560514","https://openalex.org/W1885185971","https://openalex.org/W1919542679","https://openalex.org/W1930824406","https://openalex.org/W1950594372","https://openalex.org/W1963882359","https://openalex.org/W1976416062","https://openalex.org/W1993465480","https://openalex.org/W2047920195","https://openalex.org/W2078790577","https://openalex.org/W2097074225","https://openalex.org/W2098506229","https://openalex.org/W2103504761","https://openalex.org/W2110158442","https://openalex.org/W2118963448","https://openalex.org/W2121058967","https://openalex.org/W2133665775","https://openalex.org/W2142884912","https://openalex.org/W2149669120","https://openalex.org/W2154504070","https://openalex.org/W2161516371","https://openalex.org/W2168894214","https://openalex.org/W2192954843","https://openalex.org/W2210480155","https://openalex.org/W2214802144","https://openalex.org/W2242218935","https://openalex.org/W2322480645","https://openalex.org/W2331128040","https://openalex.org/W2476548250","https://openalex.org/W2503339013","https://openalex.org/W2508741746","https://openalex.org/W2534320940","https://openalex.org/W2607333215","https://openalex.org/W2951523806","https://openalex.org/W2953066166","https://openalex.org/W2963470893","https://openalex.org/W2963606038","https://openalex.org/W3138063419","https://openalex.org/W4249022109","https://openalex.org/W6638194035"],"related_works":["https://openalex.org/W2963182372","https://openalex.org/W4318969728","https://openalex.org/W2912717253","https://openalex.org/W2954113706","https://openalex.org/W3042457149","https://openalex.org/W2969200668","https://openalex.org/W4200238602","https://openalex.org/W2952779541","https://openalex.org/W3040614056","https://openalex.org/W3101576776"],"abstract_inverted_index":{"Convolutional":[0],"neural":[1],"networks":[2],"have":[3],"recently":[4],"demonstrated":[5],"high-quality":[6,92],"reconstruction":[7],"for":[8,51],"single-image":[9],"super-resolution.":[10],"In":[11],"this":[12],"paper,":[13],"we":[14],"propose":[15],"the":[16,25,44,54,62,66,73,78,105,122,128],"Laplacian":[17],"Pyramid":[18],"Super-Resolution":[19],"Network":[20],"(LapSRN)":[21],"to":[22,53],"progressively":[23],"reconstruct":[24],"sub-band":[26],"residuals":[27],"of":[28,133],"high-resolution":[29],"images.":[30],"At":[31],"each":[32],"pyramid":[33],"level,":[34],"our":[35,95],"model":[36],"takes":[37],"coarse-resolution":[38],"feature":[39],"maps":[40],"as":[41,65],"input,":[42],"predicts":[43],"high-frequency":[45],"residuals,":[46],"and":[47,69,90,114,135],"uses":[48],"transposed":[49],"convolutions":[50],"upsampling":[52],"finer":[55],"level.":[56],"Our":[57],"method":[58],"does":[59],"not":[60],"require":[61],"bicubic":[63],"interpolation":[64],"pre-processing":[67],"step":[68],"thus":[70],"dramatically":[71],"reduces":[72],"computational":[74],"complexity.":[75],"We":[76],"train":[77],"proposed":[79,123],"LapSRN":[80],"with":[81],"deep":[82],"supervision":[83],"using":[84],"a":[85],"robust":[86],"Charbonnier":[87],"loss":[88],"function":[89],"achieve":[91],"reconstruction.":[93],"Furthermore,":[94],"network":[96],"generates":[97],"multi-scale":[98],"predictions":[99],"in":[100,131],"one":[101],"feed-forward":[102],"pass":[103],"through":[104],"progressive":[106],"reconstruction,":[107],"thereby":[108],"facilitates":[109],"resource-aware":[110],"applications.":[111],"Extensive":[112],"quantitative":[113],"qualitative":[115],"evaluations":[116],"on":[117],"benchmark":[118],"datasets":[119],"show":[120],"that":[121],"algorithm":[124],"performs":[125],"favorably":[126],"against":[127],"state-of-the-art":[129],"methods":[130],"terms":[132],"speed":[134],"accuracy.":[136]},"counts_by_year":[{"year":2026,"cited_by_count":173},{"year":2025,"cited_by_count":301},{"year":2024,"cited_by_count":416},{"year":2023,"cited_by_count":445},{"year":2022,"cited_by_count":441},{"year":2021,"cited_by_count":471},{"year":2020,"cited_by_count":390},{"year":2019,"cited_by_count":330},{"year":2018,"cited_by_count":163},{"year":2017,"cited_by_count":15}],"updated_date":"2026-11-14T11:04:58.165337","created_date":"2017-04-28T00:00:00"}
