{"id":"https://openalex.org/W2898166291","doi":"https://doi.org/10.1142/s021800141940007x","title":"Enhancing Blurred Low-Resolution Images via Exploring the Potentials of Learning-Based Super-Resolution","display_name":"Enhancing Blurred Low-Resolution Images via Exploring the Potentials of Learning-Based Super-Resolution","publication_year":2018,"publication_date":"2018-10-26","ids":{"openalex":"https://openalex.org/W2898166291","doi":"https://doi.org/10.1142/s021800141940007x","mag":"2898166291"},"language":"en","primary_location":{"id":"doi:10.1142/s021800141940007x","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s021800141940007x","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-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/A5101950655","display_name":"Wenze Shao","orcid":"https://orcid.org/0000-0001-6869-7789"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wen-Ze Shao","raw_affiliation_strings":["College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, P. R. China","National Engineering Research Center of Communications and Networking, Nanjing University of Posts and Telecommunications, Nanjing, P. R. China"],"affiliations":[{"raw_affiliation_string":"College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, P. R. China","institution_ids":["https://openalex.org/I41198531"]},{"raw_affiliation_string":"National Engineering Research Center of Communications and Networking, Nanjing University of Posts and Telecommunications, Nanjing, P. R. China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007962086","display_name":"Bing\u2010Kun Bao","orcid":"https://orcid.org/0000-0001-5956-831X"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bing-Kun Bao","raw_affiliation_strings":["College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, P. R. China"],"affiliations":[{"raw_affiliation_string":"College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, P. R. China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100394060","display_name":"Haibo Li","orcid":"https://orcid.org/0000-0001-5857-1410"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]},{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN","SE"],"is_corresponding":false,"raw_author_name":"Hai-Bo Li","raw_affiliation_strings":["College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, P. R. China","School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden"],"affiliations":[{"raw_affiliation_string":"College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, P. R. China","institution_ids":["https://openalex.org/I41198531"]},{"raw_affiliation_string":"School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101950655"],"corresponding_institution_ids":["https://openalex.org/I41198531"],"apc_list":null,"apc_paid":null,"fwci":0.5223,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.71441356,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"33","issue":"07","first_page":"1940007","last_page":"1940007"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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":0.995199978351593,"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"}},{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9922999739646912,"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/kernel","display_name":"Kernel (algebra)","score":0.6852342486381531},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6507458090782166},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5399226546287537},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.485464870929718},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4792520999908447},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.47823566198349},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44700080156326294},{"id":"https://openalex.org/keywords/norm","display_name":"Norm (philosophy)","score":0.44376662373542786},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.43985307216644287},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.43128007650375366},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.37333863973617554},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33177506923675537},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.0949302613735199}],"concepts":[{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6852342486381531},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6507458090782166},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5399226546287537},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.485464870929718},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4792520999908447},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.47823566198349},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44700080156326294},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.44376662373542786},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.43985307216644287},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.43128007650375366},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37333863973617554},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33177506923675537},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0949302613735199},{"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/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s021800141940007x","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s021800141940007x","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7900000214576721,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G1492068185","display_name":null,"funder_award_id":"61771250","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W7682646","https://openalex.org/W54257720","https://openalex.org/W1589189156","https://openalex.org/W1791560514","https://openalex.org/W1795014501","https://openalex.org/W1898468339","https://openalex.org/W1919542679","https://openalex.org/W1964096822","https://openalex.org/W1969765423","https://openalex.org/W1973788353","https://openalex.org/W1973791143","https://openalex.org/W1987075379","https://openalex.org/W2006008489","https://openalex.org/W2023902556","https://openalex.org/W2029684123","https://openalex.org/W2047123483","https://openalex.org/W2088254198","https://openalex.org/W2097713019","https://openalex.org/W2098535678","https://openalex.org/W2103824707","https://openalex.org/W2104876002","https://openalex.org/W2118963448","https://openalex.org/W2130184048","https://openalex.org/W2135238609","https://openalex.org/W2140824929","https://openalex.org/W2146782367","https://openalex.org/W2161804069","https://openalex.org/W2164551808","https://openalex.org/W2167191464","https://openalex.org/W2167307343","https://openalex.org/W2242218935","https://openalex.org/W2295477204","https://openalex.org/W2534320940","https://openalex.org/W2595890220","https://openalex.org/W2963322354","https://openalex.org/W3137074406","https://openalex.org/W4247043502"],"related_works":["https://openalex.org/W4243114048","https://openalex.org/W4237896776","https://openalex.org/W4231665652","https://openalex.org/W2000242494","https://openalex.org/W4296826658","https://openalex.org/W1837630526","https://openalex.org/W2529605301","https://openalex.org/W2335589441","https://openalex.org/W1979697693","https://openalex.org/W109499086"],"abstract_inverted_index":{"This":[0],"paper":[1],"aims":[2],"to":[3,8,37,42,67,135],"propose":[4],"a":[5,49,55,62,75,122],"candidate":[6],"solution":[7],"the":[9,20,27,70,85,90,100,106,112,115,172,174,180,191,199],"challenging":[10],"task":[11,30],"of":[12,22,162],"single-image":[13],"blind":[14,130],"super-resolution":[15],"(SR),":[16],"via":[17,98],"extensively":[18],"exploring":[19],"potentials":[21],"learning-based":[23,64],"SR":[24,65,117,126,131,184],"schemes":[25],"in":[26,148,160],"literature.":[28],"The":[29,52,128],"is":[31,96,119,133],"formulated":[32],"into":[33],"an":[34,44],"energy":[35],"functional":[36,53,193],"be":[38],"minimized":[39],"with":[40],"respect":[41],"both":[43,84,163],"intermediate":[45],"super-resolved":[46,86],"image":[47,88,118,168],"and":[48,74,89,105,142,167],"nonparametric":[50,91],"blur-kernel.":[51,92],"includes":[54],"so-called":[56],"convolutional":[57,201],"consistency":[58],"term":[59],"which":[60],"incorporates":[61],"nonblind":[63,125],"result":[66],"better":[68,137],"guide":[69],"kernel":[71],"estimation":[72,165],"process,":[73],"bi-[Formula:":[76],"see":[77,79],"text]-[Formula:":[78],"text]-norm":[80],"regularization":[81],"imposed":[82],"on":[83],"sharp":[87],"A":[93],"numerical":[94],"algorithm":[95],"deduced":[97],"coupling":[99],"splitting":[101],"augmented":[102],"Lagrangian":[103],"(SAL)":[104],"conjugate":[107],"gradient":[108],"(CG)":[109],"method.":[110,127],"With":[111],"estimated":[113],"blur-kernel,":[114],"final":[116],"reconstructed":[120],"using":[121],"simple":[123],"TV-based":[124],"proposed":[129,192],"approach":[132],"demonstrated":[134],"achieve":[136],"performance":[138],"than":[139,196],"[T.":[140],"Michaeli":[141],"M.":[143],"Irani,":[144],"Nonparametric":[145],"Blind":[146],"Super-resolution,":[147],"Proc.":[149],"IEEE":[150],"Conf.":[151],"Comput.":[152],"Vision":[153],"(IEEE":[154],"Press,":[155],"Washington,":[156],"2013),":[157],"pp.":[158],"945\u2013952.]":[159],"terms":[161],"blur-kernel":[164],"accuracy":[166],"ehancement":[169],"quality.":[170],"In":[171],"meanwhile,":[173],"experimental":[175],"results":[176],"demonstrate":[177],"surprisingly":[178],"that":[179],"local":[181],"linear":[182],"regression-based":[183],"method,":[185],"anchored":[186],"neighbor":[187],"regression":[188],"(ANR)":[189],"serves":[190],"more":[194],"appropriately":[195],"those":[197],"harnessing":[198],"deep":[200],"neural":[202],"networks.":[203]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}