{"id":"https://openalex.org/W1999820651","doi":"https://doi.org/10.1117/12.2051684","title":"Quality enhancement of low-resolution image by using natural images","display_name":"Quality enhancement of low-resolution image by using natural images","publication_year":2013,"publication_date":"2013-12-24","ids":{"openalex":"https://openalex.org/W1999820651","doi":"https://doi.org/10.1117/12.2051684","mag":"1999820651"},"language":"en","primary_location":{"id":"doi:10.1117/12.2051684","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2051684","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","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/A5087891735","display_name":"Emil Bilgazyev","orcid":null},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"E. Bilgazyev","raw_affiliation_strings":["Univ. of Houston (United States)","Univ. of Houston, United States"],"affiliations":[{"raw_affiliation_string":"Univ. of Houston (United States)","institution_ids":["https://openalex.org/I44461941"]},{"raw_affiliation_string":"Univ. of Houston, United States","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038148857","display_name":"Erol Yeniaras","orcid":null},"institutions":[{"id":"https://openalex.org/I1343551460","display_name":"The University of Texas MD Anderson Cancer Center","ror":"https://ror.org/04twxam07","country_code":"US","type":"funder","lineage":["https://openalex.org/I1343551460"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"E. Yeniaras","raw_affiliation_strings":["The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)"],"affiliations":[{"raw_affiliation_string":"The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)","institution_ids":["https://openalex.org/I1343551460"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077346765","display_name":"Ilyas Uyanik","orcid":null},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"I. Uyanik","raw_affiliation_strings":["Univ. of Houston (United States)","Univ. of Houston, United States"],"affiliations":[{"raw_affiliation_string":"Univ. of Houston (United States)","institution_ids":["https://openalex.org/I44461941"]},{"raw_affiliation_string":"Univ. of Houston, United States","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021585157","display_name":"Mahmut Unan","orcid":null},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"M. Unan","raw_affiliation_strings":["Univ. of Houston (United States)","Univ. of Houston, United States"],"affiliations":[{"raw_affiliation_string":"Univ. of Houston (United States)","institution_ids":["https://openalex.org/I44461941"]},{"raw_affiliation_string":"Univ. of Houston, United States","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076583178","display_name":"Ernst L. Leiss","orcid":null},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"E. L. Leiss","raw_affiliation_strings":["Univ. of Houston (United States)","Univ. of Houston, United States"],"affiliations":[{"raw_affiliation_string":"Univ. of Houston (United States)","institution_ids":["https://openalex.org/I44461941"]},{"raw_affiliation_string":"Univ. of Houston, United States","institution_ids":["https://openalex.org/I44461941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5087891735"],"corresponding_institution_ids":["https://openalex.org/I44461941"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.07582379,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"9067","issue":null,"first_page":"90671L","last_page":"90671L"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":1.0,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9980000257492065,"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":0.9979000091552734,"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/computer-science","display_name":"Computer science","score":0.7576267719268799},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6928843259811401},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5946641564369202},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5888420343399048},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5341823101043701},{"id":"https://openalex.org/keywords/low-resolution","display_name":"Low resolution","score":0.5303885340690613},{"id":"https://openalex.org/keywords/resolution","display_name":"Resolution (logic)","score":0.4650230407714844},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.4538671672344208},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.4527963101863861},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.426777184009552},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3609618842601776},{"id":"https://openalex.org/keywords/high-resolution","display_name":"High resolution","score":0.34970512986183167},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3258187770843506},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.06336399912834167}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7576267719268799},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6928843259811401},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5946641564369202},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5888420343399048},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5341823101043701},{"id":"https://openalex.org/C3019883945","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Low resolution","level":3,"score":0.5303885340690613},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.4650230407714844},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.4538671672344208},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.4527963101863861},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.426777184009552},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3609618842601776},{"id":"https://openalex.org/C3020199158","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"High resolution","level":2,"score":0.34970512986183167},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3258187770843506},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.06336399912834167},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2051684","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2051684","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1578352865","https://openalex.org/W1646021195","https://openalex.org/W2067042811","https://openalex.org/W2085463895","https://openalex.org/W2097074225","https://openalex.org/W2099470017","https://openalex.org/W2107384509","https://openalex.org/W2114122776","https://openalex.org/W2114398647","https://openalex.org/W2121058967","https://openalex.org/W2121927366","https://openalex.org/W2129812935","https://openalex.org/W2140257560","https://openalex.org/W2140611763","https://openalex.org/W2161516371","https://openalex.org/W2534320940","https://openalex.org/W4247319663","https://openalex.org/W6671708895","https://openalex.org/W6676981982","https://openalex.org/W6678461930","https://openalex.org/W6680804813","https://openalex.org/W6683660953","https://openalex.org/W6729003901"],"related_works":["https://openalex.org/W4296995023","https://openalex.org/W2070173637","https://openalex.org/W3118545013","https://openalex.org/W2133155333","https://openalex.org/W1516116847","https://openalex.org/W2412281349","https://openalex.org/W4212954839","https://openalex.org/W4241811109","https://openalex.org/W4401570279","https://openalex.org/W3169440385"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,103],"propose":[4],"a":[5,10,14,48,71,78],"new":[6],"algorithm":[7,51,73,95,108],"to":[8,54,76,81],"estimate":[9,77],"super-resolution":[11,94],"image":[12],"from":[13,25,39,59],"given":[15],"low-resolution":[16],"image,":[17],"by":[18],"adding":[19],"high-frequency":[20,37,84],"information":[21,38,85],"that":[22,105],"is":[23,43,52,74],"extracted":[24],"natural":[26],"high-resolution":[27],"images":[28,58],"in":[29,45,67],"the":[30,36,40,56,60,68,83,106],"training":[31,41,61],"dataset.":[32],"The":[33],"selection":[34],"of":[35,86],"dataset":[42],"accomplished":[44],"two":[46],"steps:":[47],"nearest-neighbor":[49],"search":[50],"used":[53,75],"select":[55],"closest":[57],"dataset,":[62],"which":[63],"can":[64,96],"be":[65],"implemented":[66],"GPU,":[69],"and":[70,101],"sparse-representation":[72],"weight":[79],"parameter":[80],"combine":[82],"selected":[87],"images.":[88],"This":[89],"simple":[90],"but":[91],"very":[92],"powerful":[93],"produce":[97],"state-of-the-art":[98],"results.":[99],"Qualitatively":[100],"quantitatively,":[102],"demonstrate":[104],"proposed":[107],"outperforms":[109],"existing":[110],"common":[111],"practices.":[112]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
