{"id":"https://openalex.org/W2744830798","doi":"https://doi.org/10.23919/icif.2017.8009771","title":"A novel convolutional neural network architecture for image super-resolution based on channels combination","display_name":"A novel convolutional neural network architecture for image super-resolution based on channels combination","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2744830798","doi":"https://doi.org/10.23919/icif.2017.8009771","mag":"2744830798"},"language":"en","primary_location":{"id":"doi:10.23919/icif.2017.8009771","is_oa":false,"landing_page_url":"https://doi.org/10.23919/icif.2017.8009771","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 20th International Conference on Information Fusion (Fusion)","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/A5088859214","display_name":"Cun Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cun Liu","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065766320","display_name":"Yuanxiang Li","orcid":"https://orcid.org/0000-0002-1797-3621"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanxiang Li","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103420838","display_name":"Jianhua Luo","orcid":"https://orcid.org/0009-0000-4641-8056"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianhua Luo","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yongjun Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongjun Zhou","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":0.0924,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.46653002,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"18","issue":null,"first_page":"1","last_page":"8"},"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":0.9961000084877014,"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":0.9923999905586243,"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":0.7870470285415649},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6952630877494812},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6948282718658447},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.667154848575592},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6391727328300476},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.5742849707603455},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5698652863502502},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5681652426719666},{"id":"https://openalex.org/keywords/bicubic-interpolation","display_name":"Bicubic interpolation","score":0.5582317113876343},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5518451929092407},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5180816650390625},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49371156096458435},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.4385567009449005},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.420160174369812},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4133495092391968},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3930933177471161},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3386860489845276},{"id":"https://openalex.org/keywords/linear-interpolation","display_name":"Linear interpolation","score":0.18916857242584229},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.12700703740119934}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7870470285415649},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6952630877494812},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6948282718658447},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.667154848575592},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6391727328300476},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.5742849707603455},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5698652863502502},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5681652426719666},{"id":"https://openalex.org/C49608258","wikidata":"https://www.wikidata.org/wiki/Q611705","display_name":"Bicubic interpolation","level":4,"score":0.5582317113876343},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5518451929092407},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5180816650390625},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49371156096458435},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4385567009449005},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.420160174369812},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4133495092391968},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3930933177471161},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3386860489845276},{"id":"https://openalex.org/C171836373","wikidata":"https://www.wikidata.org/wiki/Q2266329","display_name":"Linear interpolation","level":3,"score":0.18916857242584229},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.12700703740119934},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/icif.2017.8009771","is_oa":false,"landing_page_url":"https://doi.org/10.23919/icif.2017.8009771","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 20th International Conference on Information Fusion (Fusion)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5199999809265137,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W4136762","https://openalex.org/W7682646","https://openalex.org/W135113724","https://openalex.org/W935139217","https://openalex.org/W1677182931","https://openalex.org/W1791560514","https://openalex.org/W1849277567","https://openalex.org/W1885185971","https://openalex.org/W1903029394","https://openalex.org/W1906770428","https://openalex.org/W1930824406","https://openalex.org/W1950594372","https://openalex.org/W1973567017","https://openalex.org/W2037642501","https://openalex.org/W2047920195","https://openalex.org/W2097074225","https://openalex.org/W2098477387","https://openalex.org/W2098506229","https://openalex.org/W2104636679","https://openalex.org/W2108598243","https://openalex.org/W2118963448","https://openalex.org/W2121058967","https://openalex.org/W2146200771","https://openalex.org/W2149669120","https://openalex.org/W2154815154","https://openalex.org/W2164551808","https://openalex.org/W2167215970","https://openalex.org/W2214802144","https://openalex.org/W2242218935","https://openalex.org/W2250093075","https://openalex.org/W2476548250","https://openalex.org/W2503339013","https://openalex.org/W2534320940","https://openalex.org/W2950248853","https://openalex.org/W4239072543","https://openalex.org/W4377561911","https://openalex.org/W6605397647","https://openalex.org/W6624640001","https://openalex.org/W6639204139","https://openalex.org/W6674855086","https://openalex.org/W6675722231","https://openalex.org/W6676297131","https://openalex.org/W6677973867","https://openalex.org/W6684563725","https://openalex.org/W6688459880","https://openalex.org/W6691590550","https://openalex.org/W6724673846"],"related_works":["https://openalex.org/W3202223591","https://openalex.org/W3216158842","https://openalex.org/W4288103895","https://openalex.org/W3012322483","https://openalex.org/W3203374800","https://openalex.org/W2974792966","https://openalex.org/W2914107945","https://openalex.org/W4291801625","https://openalex.org/W2801811999","https://openalex.org/W2963241118"],"abstract_inverted_index":{"Several":[0],"models":[1],"based":[2,98],"on":[3,31,99,183,190],"deep":[4],"neural":[5,94],"networks":[6],"have":[7],"applied":[8],"to":[9,67,69,145],"single":[10,48],"image":[11],"super-resolution":[12,34],"and":[13,23,83,108,126,160,171,187],"obtained":[14],"great":[15],"improvements":[16],"in":[17,38,122],"terms":[18],"of":[19,56,63,74,136,185,192],"both":[20,105],"reconstruction":[21,36,58],"accuracy":[22,82,109],"computational":[24],"performance.":[25],"All":[26],"these":[27],"methods":[28],"focus":[29],"either":[30],"performing":[32],"the":[33,39,57,61,71,75,79,116,123,147,153,162],"(SR)":[35],"operation":[37],"high":[40],"resolution":[41],"(HR)":[42],"space":[43,125],"after":[44],"upscaling":[45,137],"with":[46,197],"a":[47],"filter,":[49],"usually":[50],"bicubic":[51],"interpolation,":[52],"or":[53],"optimizing":[54],"parts":[55],"pipeline.":[59],"Then":[60],"studies":[62],"network-based":[64],"model":[65],"advance":[66],"attempting":[68],"shrink":[70],"feature":[72,117,143,150],"dimension":[73],"nonlinear":[76],"mapping":[77],"considering":[78],"tradeoff":[80],"between":[81],"time":[84],"cost.":[85],"In":[86,111],"this":[87],"paper,":[88],"we":[89,113],"present":[90],"an":[91,127,134],"improved":[92],"convolutional":[93],"network":[95],"(CNN)":[96],"architecture":[97],"channels":[100],"combination,":[101],"which":[102,132],"benefits":[103],"from":[104,167],"quick":[106],"training":[107],"gain.":[110],"addition,":[112],"propose":[114],"that":[115,173],"maps":[118,151],"can":[119],"be":[120],"extracted":[121],"LR":[124],"efficient":[128],"multi-channel":[129],"convolution":[130],"layer":[131],"learns":[133],"array":[135],"filters,":[138],"specifically":[139],"trained":[140],"for":[141],"each":[142],"map,":[144],"upscale":[146],"final":[148],"HR":[149,154],"into":[152],"output.":[155],"We":[156],"explore":[157],"different":[158],"settings":[159],"evaluate":[161],"proposed":[163],"approach":[164],"using":[165],"images":[166],"publicly":[168],"available":[169],"datasets":[170],"show":[172],"it":[174],"performs":[175],"significantly":[176],"better":[177,198],"(about":[178],"+":[179,188],"0.3":[180],"dB":[181],"margin":[182],"term":[184,191],"PSNR":[186],"0.03":[189],"SSIM":[193],"than":[194],"previous":[195],"works)":[196],"visual":[199],"appearance.":[200]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}