{"id":"https://openalex.org/W2579633471","doi":"https://doi.org/10.1109/vcip.2016.7805493","title":"Smooth sparse representation for noise robust face super-resolution","display_name":"Smooth sparse representation for noise robust face super-resolution","publication_year":2016,"publication_date":"2016-11-01","ids":{"openalex":"https://openalex.org/W2579633471","doi":"https://doi.org/10.1109/vcip.2016.7805493","mag":"2579633471"},"language":"en","primary_location":{"id":"doi:10.1109/vcip.2016.7805493","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip.2016.7805493","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://stars.library.ucf.edu/scopus2015/6697","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5087165831","display_name":"Junjun Jiang","orcid":"https://orcid.org/0000-0002-5694-505X"},"institutions":[{"id":"https://openalex.org/I3125743391","display_name":"China University of Geosciences (Beijing)","ror":"https://ror.org/04q6c7p66","country_code":"CN","type":"education","lineage":["https://openalex.org/I3125743391"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Junjun Jiang","raw_affiliation_strings":["China University of Geosciences, China"],"affiliations":[{"raw_affiliation_string":"China University of Geosciences, China","institution_ids":["https://openalex.org/I3125743391"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040010053","display_name":"Jiayi Ma","orcid":"https://orcid.org/0000-0003-3264-3265"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiayi Ma","raw_affiliation_strings":["Wuhan University, China"],"affiliations":[{"raw_affiliation_string":"Wuhan University, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100418568","display_name":"Chen Chen","orcid":"https://orcid.org/0000-0003-3957-7061"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Chen","raw_affiliation_strings":["Wuhan University, China"],"affiliations":[{"raw_affiliation_string":"Wuhan University, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100741750","display_name":"Zhongyuan Wang","orcid":"https://orcid.org/0000-0002-9796-488X"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhongyuan Wang","raw_affiliation_strings":["University of Central Florida, USA"],"affiliations":[{"raw_affiliation_string":"University of Central Florida, USA","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089404454","display_name":"Tao L\u00fc","orcid":"https://orcid.org/0000-0001-8117-2012"},"institutions":[{"id":"https://openalex.org/I91125648","display_name":"Wuhan Institute of Technology","ror":"https://ror.org/04jcykh16","country_code":"CN","type":"education","lineage":["https://openalex.org/I91125648"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Lu","raw_affiliation_strings":["Wuhan Institute of Technology, China"],"affiliations":[{"raw_affiliation_string":"Wuhan Institute of Technology, China","institution_ids":["https://openalex.org/I91125648"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5087165831"],"corresponding_institution_ids":["https://openalex.org/I3125743391"],"apc_list":null,"apc_paid":null,"fwci":0.5068,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.74805083,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"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":0.9965000152587891,"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":0.9919999837875366,"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/sparse-approximation","display_name":"Sparse approximation","score":0.8950189352035522},{"id":"https://openalex.org/keywords/neural-coding","display_name":"Neural coding","score":0.8014366626739502},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6937007904052734},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6243329048156738},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6120164394378662},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.585755467414856},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5737625956535339},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5553504228591919},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5278527736663818},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.45365938544273376},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.4439978003501892},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.4321712851524353},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.41150087118148804},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3749784231185913},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3382861018180847}],"concepts":[{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.8950189352035522},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.8014366626739502},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6937007904052734},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6243329048156738},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6120164394378662},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.585755467414856},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5737625956535339},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5553504228591919},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5278527736663818},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.45365938544273376},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.4439978003501892},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4321712851524353},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.41150087118148804},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3749784231185913},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3382861018180847},{"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/vcip.2016.7805493","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip.2016.7805493","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:stars.library.ucf.edu:scopus2015-7696","is_oa":true,"landing_page_url":"https://stars.library.ucf.edu/scopus2015/6697","pdf_url":null,"source":{"id":"https://openalex.org/S4210172555","display_name":"Journal of International Crisis and Risk Communication Research","issn_l":"2576-0017","issn":["2576-0017","2576-0025"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Scopus Export 2015-2019","raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:stars.library.ucf.edu:scopus2015-7696","is_oa":true,"landing_page_url":"https://stars.library.ucf.edu/scopus2015/6697","pdf_url":null,"source":{"id":"https://openalex.org/S4210172555","display_name":"Journal of International Crisis and Risk Communication Research","issn_l":"2576-0017","issn":["2576-0017","2576-0025"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Scopus Export 2015-2019","raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/1","score":0.4399999976158142,"display_name":"No poverty"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W3219084","https://openalex.org/W1561797649","https://openalex.org/W1972002222","https://openalex.org/W1985436611","https://openalex.org/W1999457380","https://openalex.org/W2015497428","https://openalex.org/W2027325144","https://openalex.org/W2044655004","https://openalex.org/W2050874697","https://openalex.org/W2053186076","https://openalex.org/W2069165391","https://openalex.org/W2069201803","https://openalex.org/W2099470017","https://openalex.org/W2103871101","https://openalex.org/W2118963448","https://openalex.org/W2121058967","https://openalex.org/W2124141313","https://openalex.org/W2139286676","https://openalex.org/W2140257560","https://openalex.org/W2141631520","https://openalex.org/W2149760002","https://openalex.org/W2154199925","https://openalex.org/W2158046142","https://openalex.org/W2160021903","https://openalex.org/W2164307510","https://openalex.org/W2461349148","https://openalex.org/W6677973867","https://openalex.org/W6678617208","https://openalex.org/W6680467421","https://openalex.org/W6682781542","https://openalex.org/W6682935721","https://openalex.org/W6683553643","https://openalex.org/W6684221638","https://openalex.org/W6719217992"],"related_works":["https://openalex.org/W2890544631","https://openalex.org/W2067062989","https://openalex.org/W2998105788","https://openalex.org/W4205656132","https://openalex.org/W2111634407","https://openalex.org/W3004790527","https://openalex.org/W2203155458","https://openalex.org/W2783282829","https://openalex.org/W2138494306","https://openalex.org/W2539392819"],"abstract_inverted_index":{"Face":[0],"super-resolution":[1,20,94,166,181],"has":[2,46],"attracted":[3],"much":[4],"attention":[5],"in":[6,83,127],"recent":[7],"years.":[8],"Many":[9],"algorithms":[10],"have":[11],"been":[12],"proposed.":[13],"Among":[14],"them,":[15],"sparse":[16,30,66,90,108,133],"representation":[17,31,67,91,121],"based":[18,32,68,92],"face":[19,93,155,165,187],"approaches":[21,33,69],"are":[22,148],"able":[23],"to":[24,76,101,117,129,171],"achieve":[25],"competitive":[26],"performance.":[27],"However,":[28],"these":[29],"only":[34],"perform":[35],"well":[36],"under":[37,175],"the":[38,41,50,57,61,114,118,123,137,141,152,163,172,184],"condition":[39],"that":[40,96,162],"input":[42,51,62,124,142,185],"is":[43,52,145,189],"noiseless":[44,176],"or":[45],"small":[47],"noise.":[48,193],"When":[49],"corrupted":[53],"by":[54,191],"large":[55],"noise,":[56],"reconstruction":[58,78],"weights":[59],"of":[60,122,140],"LR":[63,125,143,186],"patches":[64,105],"using":[65],"will":[70],"be":[71],"seriously":[72],"unstable,":[73],"thus":[74],"leading":[75],"poor":[77],"results.":[79],"To":[80],"this":[81,84],"end,":[82],"paper,":[85],"we":[86,112],"propose":[87],"a":[88,98,131],"novel":[89],"approach":[95],"incorporates":[97],"smooth":[99],"prior":[100],"enforce":[102],"similar":[103,107],"training":[104],"having":[106],"coding":[109],"coefficients.":[110],"Specifically,":[111],"introduce":[113],"fused":[115],"Lasso":[116],"least":[119],"squares":[120],"image":[126,144,188],"order":[128],"obtain":[130],"stable":[132],"representation,":[134],"especially":[135],"when":[136,183],"noise":[138],"level":[139],"high.":[146],"Experiments":[147],"carried":[149],"out":[150],"on":[151],"benchmark":[153],"FEI":[154],"dataset.":[156],"Visual":[157],"and":[158,178],"quantitative":[159],"comparisons":[160],"show":[161],"proposed":[164],"method":[167],"achieves":[168],"comparable":[169],"performance":[170],"state-of-the-art":[173],"methods":[174],"condition,":[177],"yields":[179],"superior":[180],"results":[182],"contaminated":[190],"strong":[192]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
