{"id":"https://openalex.org/W2889862971","doi":"https://doi.org/10.1109/icip.2018.8451786","title":"Deep Learning Based Super Resolution Using Significant and General Regions","display_name":"Deep Learning Based Super Resolution Using Significant and General Regions","publication_year":2018,"publication_date":"2018-09-07","ids":{"openalex":"https://openalex.org/W2889862971","doi":"https://doi.org/10.1109/icip.2018.8451786","mag":"2889862971"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2018.8451786","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2018.8451786","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 25th IEEE International Conference on Image Processing (ICIP)","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/A5065902596","display_name":"Liling Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhao Liling","raw_affiliation_strings":["Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing, Jiangsu"],"affiliations":[{"raw_affiliation_string":"Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing, Jiangsu","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101400901","display_name":"Zelin Zhang","orcid":"https://orcid.org/0000-0003-3644-4458"},"institutions":[{"id":"https://openalex.org/I200845125","display_name":"Nanjing University of Information Science and Technology","ror":"https://ror.org/02y0rxk19","country_code":"CN","type":"education","lineage":["https://openalex.org/I200845125"]},{"id":"https://openalex.org/I929597975","display_name":"National University of Sciences and Technology","ror":"https://ror.org/03w2j5y17","country_code":"PK","type":"education","lineage":["https://openalex.org/I929597975"]}],"countries":["CN","PK"],"is_corresponding":false,"raw_author_name":"Zhang Zelin","raw_affiliation_strings":["School of Information and Control Engineering, NUIST, Nanjing, Jiangsu"],"affiliations":[{"raw_affiliation_string":"School of Information and Control Engineering, NUIST, Nanjing, Jiangsu","institution_ids":["https://openalex.org/I929597975","https://openalex.org/I200845125"]}]},{"author_position":"last","author":{"id":null,"display_name":"Sun Quansen","orcid":null},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sun Quansen","raw_affiliation_strings":["School of Cornputer Science and Technology, NUST, Nanjing, Jiangsu"],"affiliations":[{"raw_affiliation_string":"School of Cornputer Science and Technology, NUST, Nanjing, Jiangsu","institution_ids":["https://openalex.org/I36399199"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5065902596"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.09217339,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"521","issue":null,"first_page":"2516","last_page":"2520"},"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9970999956130981,"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":0.9965000152587891,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8435118198394775},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.744384229183197},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.637138843536377},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.6236036419868469},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5591214895248413},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5526321530342102},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.509558916091919},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.48420026898384094},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4661720097064972},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4600626528263092},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4371541738510132},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42882591485977173},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.346942663192749},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.338179349899292}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8435118198394775},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.744384229183197},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.637138843536377},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6236036419868469},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5591214895248413},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5526321530342102},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.509558916091919},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.48420026898384094},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4661720097064972},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4600626528263092},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4371541738510132},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42882591485977173},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.346942663192749},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.338179349899292},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2018.8451786","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2018.8451786","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 25th IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W54257720","https://openalex.org/W1658472922","https://openalex.org/W1885185971","https://openalex.org/W1974973438","https://openalex.org/W1975965915","https://openalex.org/W2028123505","https://openalex.org/W2047920195","https://openalex.org/W2049352069","https://openalex.org/W2056935208","https://openalex.org/W2121058967","https://openalex.org/W2161516371","https://openalex.org/W2214802144","https://openalex.org/W2242218935","https://openalex.org/W2414869197","https://openalex.org/W2476548250","https://openalex.org/W2503339013","https://openalex.org/W2505593925","https://openalex.org/W2604469346","https://openalex.org/W2747898905","https://openalex.org/W2919115771","https://openalex.org/W2951523806","https://openalex.org/W2963470893","https://openalex.org/W4377561911","https://openalex.org/W6683660953","https://openalex.org/W6725212907"],"related_works":["https://openalex.org/W4390608645","https://openalex.org/W4247566972","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W3185156046"],"abstract_inverted_index":{"Today,":[0],"Big":[1,47,159],"Data":[2,160],"brings":[3],"benefits":[4],"to":[5,33,46,58,141,149],"many":[6,163],"areas":[7],"of":[8,16,105,119],"scientific":[9],"research.":[10],"However,":[11],"processing":[12],"these":[13],"large":[14,25],"amounts":[15],"data":[17,61,73,122],"often":[18],"requires":[19],"extensive":[20],"computing":[21],"time":[22,185],"and":[23,63,76,87,99,124,169,186],"a":[24,51,91,106,133,188],"storage":[26],"space.":[27],"Global":[28],"feature":[29],"analysis":[30,74],"is":[31,43,55,113,147],"considered":[32],"be":[34,138,154],"universal":[35],"in":[36],"traditional":[37],"super":[38,93,134],"resolution":[39,94,135],"methods,":[40],"but":[41,151],"it":[42,54,152],"not":[44,56],"applicable":[45],"Data.":[48],"There":[49],"remains":[50],"viewpoint":[52],"that":[53,96,177],"necessary":[57],"address":[59],"all":[60],"equally":[62],"impartially.":[64],"Focusing":[65],"on":[66,115,166],"useful":[67],"information":[68],"can":[69,153,181],"make":[70],"the":[71,84,103,110,116,120,125,158,167,170,183],"massive":[72],"possible":[75],"more":[77],"effective.":[78],"In":[79],"this":[80],"paper,":[81],"we":[82,89],"consider":[83],"significant":[85,98,117,129],"regions,":[86],"thus,":[88],"propose":[90],"new":[92,179],"approach":[95,161,180],"uses":[97],"general":[100],"information.":[101],"Under":[102],"framework":[104],"convolutional":[107],"neural":[108],"network,":[109],"training":[111,121],"process":[112,127],"performed":[114],"parts":[118,130],"set,":[123],"reconstruction":[126],"considers":[128],"separately;":[131],"then,":[132],"image":[136],"will":[137],"obtained":[139],"according":[140],"each":[142],"different":[143],"demand.":[144],"This":[145],"concept":[146],"easy":[148],"understand,":[150],"achieved":[155],"only":[156],"via":[157],"with":[162],"similar":[164],"images":[165],"Internet":[168],"effective":[171],"deep":[172],"learning":[173],"algorithm.":[174],"Experiments":[175],"show":[176],"our":[178],"reduce":[182],"testing":[184],"obtain":[187],"high-quality":[189],"reconstructed":[190],"image.":[191]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}
