{"id":"https://openalex.org/W2792597784","doi":"https://doi.org/10.1109/icip.2017.8296422","title":"Image super-resolution via deep dilated convolutional networks","display_name":"Image super-resolution via deep dilated convolutional networks","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2792597784","doi":"https://doi.org/10.1109/icip.2017.8296422","mag":"2792597784"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2017.8296422","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296422","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 Image Processing (ICIP)","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/A5017829201","display_name":"Zehao Huang","orcid":"https://orcid.org/0000-0003-1653-208X"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I118612203","display_name":"Hunan Police Academy","ror":"https://ror.org/02gh10772","country_code":"CN","type":"education","lineage":["https://openalex.org/I118612203"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zehao Huang","raw_affiliation_strings":["Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Police Academy","NLPR, Chinese Academy of Sciences"],"affiliations":[{"raw_affiliation_string":"Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Police Academy","institution_ids":["https://openalex.org/I118612203"]},{"raw_affiliation_string":"NLPR, Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100629700","display_name":"Lingfeng Wang","orcid":"https://orcid.org/0000-0003-3707-0267"},"institutions":[{"id":"https://openalex.org/I118612203","display_name":"Hunan Police Academy","ror":"https://ror.org/02gh10772","country_code":"CN","type":"education","lineage":["https://openalex.org/I118612203"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingfeng Wang","raw_affiliation_strings":["Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Police Academy","NLPR, Chinese Academy of Sciences"],"affiliations":[{"raw_affiliation_string":"Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Police Academy","institution_ids":["https://openalex.org/I118612203"]},{"raw_affiliation_string":"NLPR, Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100675867","display_name":"Gaofeng Meng","orcid":"https://orcid.org/0000-0002-7103-6321"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gaofeng Meng","raw_affiliation_strings":["NLPR, Chinese Academy of Sciences"],"affiliations":[{"raw_affiliation_string":"NLPR, Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100435212","display_name":"Chunhong Pan","orcid":"https://orcid.org/0000-0001-7433-4474"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunhong Pan","raw_affiliation_strings":["NLPR, Chinese Academy of Sciences"],"affiliations":[{"raw_affiliation_string":"NLPR, Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5017829201"],"corresponding_institution_ids":["https://openalex.org/I118612203","https://openalex.org/I19820366"],"apc_list":null,"apc_paid":null,"fwci":0.8192,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.82984693,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"953","last_page":"957"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9983000159263611,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9962999820709229,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8003069162368774},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.7291839122772217},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6961259841918945},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6395137906074524},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5410685539245605},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4838777780532837},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4792363941669464},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4330711364746094},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3858751356601715},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.352946937084198},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3369608223438263},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1429484784603119},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10159781575202942}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8003069162368774},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.7291839122772217},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6961259841918945},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6395137906074524},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5410685539245605},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4838777780532837},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4792363941669464},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4330711364746094},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3858751356601715},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.352946937084198},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3369608223438263},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1429484784603119},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10159781575202942},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2017.8296422","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296422","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 Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4000000059604645}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W54257720","https://openalex.org/W935139217","https://openalex.org/W1522301498","https://openalex.org/W1552869711","https://openalex.org/W1667652561","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W1791560514","https://openalex.org/W1885185971","https://openalex.org/W1919542679","https://openalex.org/W1995228944","https://openalex.org/W2015374556","https://openalex.org/W2047920195","https://openalex.org/W2118963448","https://openalex.org/W2121058967","https://openalex.org/W2121927366","https://openalex.org/W2137290314","https://openalex.org/W2155893237","https://openalex.org/W2194775991","https://openalex.org/W2214802144","https://openalex.org/W2242218935","https://openalex.org/W2476548250","https://openalex.org/W2503339013","https://openalex.org/W2534320940","https://openalex.org/W2592088493","https://openalex.org/W2962835968","https://openalex.org/W2963840672","https://openalex.org/W2964121744","https://openalex.org/W4302296459","https://openalex.org/W4377561911","https://openalex.org/W6624640001","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6638194035","https://openalex.org/W6696085341","https://openalex.org/W6724673846"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W2964954556","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"techniques":[2],"have":[3,14],"been":[4],"successfully":[5],"applied":[6],"in":[7,110],"single":[8,70],"image":[9,71],"super-resolution":[10],"(SR).":[11],"Recently,":[12],"researches":[13],"shown":[15],"that":[16],"increasing":[17,41,90],"the":[18,86,94],"depth":[19],"of":[20,85,96],"network":[21,127,137],"can":[22,101],"significantly":[23],"improve":[24],"SR":[25,31,129],"performance.":[26],"Very":[27],"deep":[28,67,118,143],"networks":[29,68,100,144],"for":[30,69,128],"achieved":[32],"a":[33,59,116,122],"large":[34,103],"improvement":[35],"than":[36,141],"former":[37],"methods.":[38],"However,":[39],"simply":[40],"depths":[42],"basically":[43],"introduce":[44],"more":[45],"parameters":[46],"and":[47,61,106,145],"this":[48,55],"lead":[49],"to":[50,64],"cumbersome":[51],"computational":[52],"cost.":[53],"In":[54],"paper,":[56],"we":[57,120],"present":[58],"general":[60],"effective":[62],"method":[63,74],"accelerate":[65],"very":[66,117],"SR.":[72],"Our":[73],"is":[75],"based":[76],"on":[77,115],"dilated":[78,97,125],"convolution":[79],"operation,":[80],"which":[81],"support":[82],"exponential":[83],"expansion":[84],"receptive":[87,104],"field":[88,105],"without":[89],"filter":[91],"size.":[92],"With":[93],"help":[95],"convolution,":[98],"shallow":[99,136],"achieve":[102],"exploit":[107],"contextual":[108],"information":[109],"an":[111],"efficient":[112],"way.":[113],"Based":[114],"network,":[119],"propose":[121],"12":[123],"layers":[124],"convolutional":[126],"(DCNSR).":[130],"While":[131],"accelerating":[132],"2x":[133],"speed,":[134],"our":[135],"achieves":[138],"better":[139],"performance":[140],"original":[142],"shows":[146],"state-of-the-art":[147],"reconstructed":[148],"results.":[149]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}