{"id":"https://openalex.org/W2901694993","doi":"https://doi.org/10.1109/icmlc.2018.8526924","title":"Weighted Patches Based Face Super-Resolution Via Adaboost","display_name":"Weighted Patches Based Face Super-Resolution Via Adaboost","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2901694993","doi":"https://doi.org/10.1109/icmlc.2018.8526924","mag":"2901694993"},"language":"en","primary_location":{"id":"doi:10.1109/icmlc.2018.8526924","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc.2018.8526924","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","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/A5002135455","display_name":"Shanjun Mao","orcid":"https://orcid.org/0000-0001-9212-4761"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Shan-Jun Mao","raw_affiliation_strings":["Department of Statistics, The Chinese University of Hong Kong, Hong Kong, PR, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, The Chinese University of Hong Kong, Hong Kong, PR, China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018955596","display_name":"Da Zhou","orcid":"https://orcid.org/0000-0002-0272-6644"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Da Zhou","raw_affiliation_strings":["School of Mathematical Sciences, Xiamen University, Xiamen, PR, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Xiamen University, Xiamen, PR, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100773052","display_name":"Yiping Zhang","orcid":"https://orcid.org/0000-0002-7944-782X"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi-Ping Zhang","raw_affiliation_strings":["Software School, Xiamen University, Xiamen, PR, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Software School, Xiamen University, Xiamen, PR, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100419458","display_name":"Zhihong Zhang","orcid":"https://orcid.org/0000-0002-0542-0640"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhi-Hong Zhang","raw_affiliation_strings":["Software School, Xiamen University, Xiamen, PR, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Software School, Xiamen University, Xiamen, PR, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102946364","display_name":"Jingjing Cao","orcid":"https://orcid.org/0000-0002-3483-6100"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing-Jing Cao","raw_affiliation_strings":["School of Logistics Engineering, Wuhan University of Technology, Wuhan, PR, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Logistics Engineering, Wuhan University of Technology, Wuhan, PR, China","institution_ids":["https://openalex.org/I196699116"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.212,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.57554149,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"234","last_page":"239"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9891999959945679,"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":0.9865000247955322,"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/adaboost","display_name":"AdaBoost","score":0.7988622188568115},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6788598299026489},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6685916185379028},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.640273928642273},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.623103678226471},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.4641242027282715},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.43136242032051086},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.42746207118034363},{"id":"https://openalex.org/keywords/resolution","display_name":"Resolution (logic)","score":0.4256129264831543},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.41350075602531433},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2784225046634674},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.11332014203071594}],"concepts":[{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.7988622188568115},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6788598299026489},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6685916185379028},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.640273928642273},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.623103678226471},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.4641242027282715},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.43136242032051086},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.42746207118034363},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.4256129264831543},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.41350075602531433},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2784225046634674},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.11332014203071594},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmlc.2018.8526924","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmlc.2018.8526924","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1571836963","https://openalex.org/W1791560514","https://openalex.org/W1988790447","https://openalex.org/W1996675432","https://openalex.org/W2033419168","https://openalex.org/W2052984131","https://openalex.org/W2120432039","https://openalex.org/W2121058967","https://openalex.org/W2133665775","https://openalex.org/W2150081556","https://openalex.org/W2160547390","https://openalex.org/W2553884582","https://openalex.org/W2994340921","https://openalex.org/W3203006154"],"related_works":["https://openalex.org/W3039673966","https://openalex.org/W4293699968","https://openalex.org/W2002351707","https://openalex.org/W2035096001","https://openalex.org/W4312843811","https://openalex.org/W2136989247","https://openalex.org/W1973963881","https://openalex.org/W1580457994","https://openalex.org/W2384651879","https://openalex.org/W2912726270"],"abstract_inverted_index":{"To":[0],"alleviate":[1],"the":[2,35,46,72,77,88,105,123,126],"blurring":[3],"effect":[4],"of":[5,14,74,125],"super-resolved":[6],"faces":[7],"in":[8,96,113],"super-resolution":[9,68],"(SR)":[10],"field,":[11],"a":[12,51,56,66],"number":[13],"sparse":[15],"representation":[16],"methods":[17],"have":[18,42],"been":[19],"proposed":[20,127],"recently.":[21],"However,":[22],"current":[23,97],"researches":[24],"normally":[25],"treat":[26],"all":[27],"facial":[28,39,81],"patches":[29,40,108],"equally":[30],"and":[31,93],"do":[32],"not":[33],"consider":[34],"fact":[36],"that":[37,49],"different":[38],"may":[41],"unequal":[43],"contributions":[44],"to":[45,64,87,104],"SR.":[47],"Regarding":[48],"AdaBoost,":[50],"classical":[52],"ensemble":[53],"method,":[54],"has":[55],"natural":[57],"weighted":[58,84],"update":[59],"scheme,":[60],"this":[61],"paper":[62],"aims":[63],"develop":[65],"weighted-patch":[67],"approach":[69],"based":[70],"on":[71,119],"framework":[73],"AdaBoost.":[75],"In":[76],"training":[78],"phase,":[79],"each":[80],"patch":[82,92,95],"is":[83],"automatically":[85],"according":[86],"difference":[89],"between":[90],"reconstructed":[91],"original":[94],"iteration,":[98],"which":[99],"can":[100],"assign":[101],"more":[102],"weights":[103],"worse":[106],"performed":[107],"with":[109],"lower":[110],"reconstruction":[111],"power":[112],"next":[114],"iteration.":[115],"The":[116],"experimental":[117],"results":[118],"two":[120],"benchmarks":[121],"demonstrate":[122],"effectiveness":[124],"approach.":[128]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
