{"id":"https://openalex.org/W3035022492","doi":"https://doi.org/10.1109/cvpr42600.2020.00583","title":"Learning Texture Transformer Network for Image Super-Resolution","display_name":"Learning Texture Transformer Network for Image Super-Resolution","publication_year":2020,"publication_date":"2020-06-01","ids":{"openalex":"https://openalex.org/W3035022492","doi":"https://doi.org/10.1109/cvpr42600.2020.00583","mag":"3035022492"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr42600.2020.00583","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr42600.2020.00583","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","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/A5030224670","display_name":"Fuzhi Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fuzhi Yang","raw_affiliation_strings":["Department of Computer Science and Engineering, MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101953782","display_name":"Huan Yang","orcid":"https://orcid.org/0000-0002-8612-0702"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huan Yang","raw_affiliation_strings":["Microsoft Research, Beijing, P.R. China"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Beijing, P.R. China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072029041","display_name":"Jianlong Fu","orcid":"https://orcid.org/0000-0002-1025-2012"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianlong Fu","raw_affiliation_strings":["Microsoft Research, Beijing, P.R. China"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Beijing, P.R. China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102899381","display_name":"Hongtao Lu","orcid":"https://orcid.org/0000-0003-2300-3039"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongtao Lu","raw_affiliation_strings":["Department of Computer Science and Engineering, MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101666011","display_name":"Baining Guo","orcid":"https://orcid.org/0000-0001-8349-8868"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baining Guo","raw_affiliation_strings":["Microsoft Research, Beijing, P.R. China"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Beijing, P.R. China","institution_ids":["https://openalex.org/I4210113369"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5030224670"],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":57.6121,"has_fulltext":false,"cited_by_count":927,"citation_normalized_percentile":{"value":0.99940318,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"5790","last_page":"5799"},"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":0.9829000234603882,"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":0.9796000123023987,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7351411581039429},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7114871144294739},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6354272365570068},{"id":"https://openalex.org/keywords/image-texture","display_name":"Image texture","score":0.5828291773796082},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5522333979606628},{"id":"https://openalex.org/keywords/extractor","display_name":"Extractor","score":0.5211043953895569},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5190649628639221},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46094828844070435},{"id":"https://openalex.org/keywords/magnification","display_name":"Magnification","score":0.45619139075279236},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4560149013996124},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.45362064242362976},{"id":"https://openalex.org/keywords/texture-compression","display_name":"Texture compression","score":0.43089765310287476},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3221716284751892},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.25112563371658325},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.0749761164188385}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7351411581039429},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7114871144294739},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6354272365570068},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.5828291773796082},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5522333979606628},{"id":"https://openalex.org/C117978034","wikidata":"https://www.wikidata.org/wiki/Q5422192","display_name":"Extractor","level":2,"score":0.5211043953895569},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5190649628639221},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46094828844070435},{"id":"https://openalex.org/C4144372","wikidata":"https://www.wikidata.org/wiki/Q675287","display_name":"Magnification","level":2,"score":0.45619139075279236},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4560149013996124},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.45362064242362976},{"id":"https://openalex.org/C54243161","wikidata":"https://www.wikidata.org/wiki/Q39333","display_name":"Texture compression","level":5,"score":0.43089765310287476},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3221716284751892},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.25112563371658325},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0749761164188385},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvpr42600.2020.00583","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr42600.2020.00583","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","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":60,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1885185971","https://openalex.org/W1930824406","https://openalex.org/W1970185025","https://openalex.org/W1976416062","https://openalex.org/W1976598894","https://openalex.org/W1991867157","https://openalex.org/W2036062360","https://openalex.org/W2087380704","https://openalex.org/W2097074225","https://openalex.org/W2099471712","https://openalex.org/W2150081556","https://openalex.org/W2192954843","https://openalex.org/W2194775991","https://openalex.org/W2214802144","https://openalex.org/W2242218935","https://openalex.org/W2331128040","https://openalex.org/W2476548250","https://openalex.org/W2503339013","https://openalex.org/W2526558307","https://openalex.org/W2554753600","https://openalex.org/W2747898905","https://openalex.org/W2780544323","https://openalex.org/W2799062770","https://openalex.org/W2866634454","https://openalex.org/W2883996939","https://openalex.org/W2891158090","https://openalex.org/W2894343975","https://openalex.org/W2919046835","https://openalex.org/W2940262938","https://openalex.org/W2954930822","https://openalex.org/W2962737939","https://openalex.org/W2962879692","https://openalex.org/W2963037581","https://openalex.org/W2963231084","https://openalex.org/W2963372104","https://openalex.org/W2963403868","https://openalex.org/W2963446712","https://openalex.org/W2963470893","https://openalex.org/W2963966654","https://openalex.org/W2964101377","https://openalex.org/W2964125708","https://openalex.org/W2983339877","https://openalex.org/W3101659800","https://openalex.org/W4241944036","https://openalex.org/W4295521014","https://openalex.org/W4320013936","https://openalex.org/W4385245566","https://openalex.org/W6642769500","https://openalex.org/W6648316099","https://openalex.org/W6702130928","https://openalex.org/W6724673846","https://openalex.org/W6729494632","https://openalex.org/W6735913928","https://openalex.org/W6739901393","https://openalex.org/W6752237900","https://openalex.org/W6753074096","https://openalex.org/W6753407927","https://openalex.org/W6754405603","https://openalex.org/W6754799269"],"related_works":["https://openalex.org/W2362774332","https://openalex.org/W2166724105","https://openalex.org/W1983661653","https://openalex.org/W2093957690","https://openalex.org/W2003873690","https://openalex.org/W2165772202","https://openalex.org/W201295475","https://openalex.org/W2115502907","https://openalex.org/W2035842265","https://openalex.org/W2359713774"],"abstract_inverted_index":{"We":[0],"study":[1],"on":[2,195],"image":[3,102],"super-resolution":[4],"(SR),":[5],"which":[6,56,78,142,171],"aims":[7],"to":[8,36,44,48,181],"recover":[9],"realistic":[10],"textures":[11,32,52],"from":[12,53,175,179],"a":[13,68,91,106,112,116,123,130,168],"low-resolution":[14],"(LR)":[15],"image.":[16],"Recent":[17],"progress":[18],"has":[19],"been":[20],"made":[21],"by":[22,110,149],"taking":[23],"high-resolution":[24,50],"images":[25,83],"as":[26,86],"references":[27],"(Ref),":[28],"so":[29],"that":[30,187],"relevant":[31],"can":[33,146,156,163],"be":[34,147,157,164],"transferred":[35],"LR":[37,80,137],"images.":[38],"However,":[39],"existing":[40],"SR":[41],"approaches":[42,59,194],"neglect":[43],"use":[45],"attention":[46],"mechanisms":[47],"transfer":[49],"(HR)":[51],"Ref":[54,82,139],"images,":[55,140],"limits":[57],"these":[58],"in":[60,77,90,141,167],"challenging":[61],"cases.":[62],"In":[63],"this":[64],"paper,":[65],"we":[66],"propose":[67],"novel":[69],"Texture":[70],"Transformer":[71],"Network":[72],"for":[73,101,119,126],"Image":[74],"Super-Resolution":[75],"(TTSR),":[76],"the":[79],"and":[81,88,122,138,151,198],"are":[84],"formulated":[85],"queries":[87],"keys":[89],"transformer,":[92],"respectively.":[93],"TTSR":[94,188],"consists":[95],"of":[96],"four":[97],"closely-related":[98],"modules":[99],"optimized":[100],"generation":[103],"tasks,":[104],"including":[105],"learnable":[107],"texture":[108,120,127,154,161,173],"extractor":[109],"DNN,":[111],"relevance":[113],"embedding":[114],"module,":[115],"hard-attention":[117],"module":[118,125],"transfer,":[121],"soft-attention":[124],"synthesis.":[128],"Such":[129],"design":[131],"encourages":[132],"joint":[133],"feature":[134,144],"learning":[135],"across":[136],"deep":[143],"correspondences":[145],"discovered":[148],"attention,":[150],"thus":[152],"accurate":[153],"features":[155],"transferred.":[158],"The":[159],"proposed":[160],"transformer":[162],"further":[165],"stacked":[166],"cross-scale":[169],"way,":[170],"enables":[172],"recovery":[174],"different":[176],"levels":[177],"(e.g.,":[178],"1x":[180],"4x":[182],"magnification).":[183],"Extensive":[184],"experiments":[185],"show":[186],"achieves":[189],"significant":[190],"improvements":[191],"over":[192],"state-of-the-art":[193],"both":[196],"quantitative":[197],"qualitative":[199],"evaluations.":[200]},"counts_by_year":[{"year":2026,"cited_by_count":15},{"year":2025,"cited_by_count":133},{"year":2024,"cited_by_count":192},{"year":2023,"cited_by_count":228},{"year":2022,"cited_by_count":215},{"year":2021,"cited_by_count":136},{"year":2020,"cited_by_count":8}],"updated_date":"2026-04-11T08:14:18.477133","created_date":"2025-10-10T00:00:00"}