{"id":"https://openalex.org/W3111132958","doi":"https://doi.org/10.1109/jstsp.2020.3045282","title":"Multi-Scale Image Super-Resolution Via a Single Extendable Deep Network","display_name":"Multi-Scale Image Super-Resolution Via a Single Extendable Deep Network","publication_year":2020,"publication_date":"2020-12-17","ids":{"openalex":"https://openalex.org/W3111132958","doi":"https://doi.org/10.1109/jstsp.2020.3045282","mag":"3111132958"},"language":"en","primary_location":{"id":"doi:10.1109/jstsp.2020.3045282","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstsp.2020.3045282","pdf_url":null,"source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Signal Processing","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/A5027946382","display_name":"Huanrong Zhang","orcid":"https://orcid.org/0000-0003-3830-3480"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Huanrong Zhang","raw_affiliation_strings":["School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055381291","display_name":"Jie Xiao","orcid":"https://orcid.org/0000-0001-6004-218X"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Xiao","raw_affiliation_strings":["School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039127802","display_name":"Zhi Jin","orcid":"https://orcid.org/0000-0001-9670-7366"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhi Jin","raw_affiliation_strings":["School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5027946382"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":1.8563,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.88136049,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"15","issue":"2","first_page":"253","last_page":"263"},"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9972000122070312,"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":0.9968000054359436,"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":0.803101122379303},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.722087025642395},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6800557971000671},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5866826176643372},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5693036913871765},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5678221583366394},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5570822954177856},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.5258791446685791},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5211610794067383},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4983978271484375},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4571947157382965},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3841494917869568},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.33600032329559326},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11839494109153748}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.803101122379303},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.722087025642395},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6800557971000671},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5866826176643372},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5693036913871765},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5678221583366394},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5570822954177856},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.5258791446685791},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5211610794067383},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4983978271484375},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4571947157382965},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3841494917869568},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33600032329559326},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11839494109153748},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","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},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jstsp.2020.3045282","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstsp.2020.3045282","pdf_url":null,"source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Signal Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6495957267","display_name":null,"funder_award_id":"61701313","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6721293684","display_name":null,"funder_award_id":"62071500","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":71,"referenced_works":["https://openalex.org/W54257720","https://openalex.org/W134193804","https://openalex.org/W935139217","https://openalex.org/W1522301498","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W1791560514","https://openalex.org/W1901129140","https://openalex.org/W1930824406","https://openalex.org/W2020455334","https://openalex.org/W2047920195","https://openalex.org/W2099471712","https://openalex.org/W2107589634","https://openalex.org/W2121927366","https://openalex.org/W2133665775","https://openalex.org/W2159522388","https://openalex.org/W2192954843","https://openalex.org/W2194775991","https://openalex.org/W2214802144","https://openalex.org/W2242218935","https://openalex.org/W2317983151","https://openalex.org/W2417716951","https://openalex.org/W2476548250","https://openalex.org/W2503339013","https://openalex.org/W2607041014","https://openalex.org/W2615439916","https://openalex.org/W2741137940","https://openalex.org/W2741196023","https://openalex.org/W2747898905","https://openalex.org/W2776107444","https://openalex.org/W2780544323","https://openalex.org/W2866634454","https://openalex.org/W2895518292","https://openalex.org/W2895598217","https://openalex.org/W2908513290","https://openalex.org/W2913182483","https://openalex.org/W2962785568","https://openalex.org/W2963031226","https://openalex.org/W2963372104","https://openalex.org/W2963470893","https://openalex.org/W2963494934","https://openalex.org/W2963610452","https://openalex.org/W2964121744","https://openalex.org/W2964125708","https://openalex.org/W2965669158","https://openalex.org/W2970971581","https://openalex.org/W2971061988","https://openalex.org/W2976718572","https://openalex.org/W2994323562","https://openalex.org/W2998506323","https://openalex.org/W3008359818","https://openalex.org/W3011005573","https://openalex.org/W3012263291","https://openalex.org/W3031778801","https://openalex.org/W3032190803","https://openalex.org/W3034328879","https://openalex.org/W3035302306","https://openalex.org/W3099091830","https://openalex.org/W3101659800","https://openalex.org/W3105328221","https://openalex.org/W4295312788","https://openalex.org/W4320013936","https://openalex.org/W6602211262","https://openalex.org/W6624640001","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6639824700","https://openalex.org/W6724673846","https://openalex.org/W6738464239","https://openalex.org/W6753074096","https://openalex.org/W6766978945"],"related_works":["https://openalex.org/W2382174632","https://openalex.org/W2129959498","https://openalex.org/W2964954556","https://openalex.org/W4386858688","https://openalex.org/W2982536526","https://openalex.org/W2077021924","https://openalex.org/W4380302312","https://openalex.org/W3008689640","https://openalex.org/W4385338604","https://openalex.org/W3081626085"],"abstract_inverted_index":{"Deep":[0],"neural":[1],"networks":[2],"have":[3],"achieved":[4],"remarkable":[5],"success":[6],"in":[7,13],"single":[8],"image":[9,16,36],"super-resolution":[10],"(SISR).":[11],"However,":[12],"most":[14],"cases,":[15],"SR":[17,37,58],"with":[18,147],"different":[19,25],"scale":[20],"factors":[21],"is":[22,72,85,98,113,127,156],"considered":[23],"as":[24],"tasks":[26],"and":[27,40,51,78,151],"solved":[28],"by":[29,60],"training":[30],"specific":[31],"models.":[32],"It":[33],"makes":[34],"the":[35,66,89,95,102,110,131,140],"applications":[38],"inefficient":[39],"tedious.":[41],"Hence,":[42],"to":[43,55,115,129],"tackle":[44],"these":[45],"problems,":[46],"we":[47],"propose":[48],"a":[49,121,148],"lightweight":[50,152],"fast":[52,149],"network":[53,71,132],"(MSWSR)":[54],"implement":[56],"multi-scale":[57],"simultaneously":[59],"learning":[61],"multi-level":[62],"wavelet":[63,92,107],"coefficients":[64],"of":[65,74,106],"target":[67],"image.":[68],"The":[69,82,154],"proposed":[70,128,141],"composed":[73],"one":[75,79],"CNN":[76,83],"part":[77,84,97,112],"RNN":[80,96,111],"part.":[81],"used":[86,99],"for":[87,100],"predicting":[88,101],"highest-level":[90],"low-frequency":[91],"coefficients,":[93],"while":[94],"rest":[103],"frequency":[104],"bands":[105],"coefficients.":[108],"Moreover,":[109],"extendable":[114],"more":[116],"scales.":[117],"For":[118],"further":[119],"lightweight,":[120],"non-square":[122],"(side":[123],"window)":[124],"convolution":[125],"kernel":[126],"reduce":[130],"parameters.":[133],"Experiments":[134],"on":[135],"commonly-used":[136],"datasets":[137],"demonstrate":[138],"that":[139],"method":[142],"achieves":[143],"favorable":[144],"reconstruction":[145],"performance":[146],"speed":[150],"network.":[153],"code":[155],"available":[157],"at":[158],"https://github.com/FVL2020/MSWSR.":[159]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
