{"id":"https://openalex.org/W3113770758","doi":"https://doi.org/10.23919/eusipco47968.2020.9287713","title":"Gated Recurrent Networks for Video Super Resolution","display_name":"Gated Recurrent Networks for Video Super Resolution","publication_year":2020,"publication_date":"2020-12-18","ids":{"openalex":"https://openalex.org/W3113770758","doi":"https://doi.org/10.23919/eusipco47968.2020.9287713","mag":"3113770758"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco47968.2020.9287713","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco47968.2020.9287713","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 28th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5046773255","display_name":"Santiago L\u00f3pez-Tapia","orcid":"https://orcid.org/0000-0003-2090-7446"},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Santiago Lopez-Tapia","raw_affiliation_strings":["University of Granada, Granada, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Granada, Granada, Spain","institution_ids":["https://openalex.org/I173304897"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043771227","display_name":"Alice Lucas","orcid":"https://orcid.org/0000-0001-5914-4154"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alice Lucas","raw_affiliation_strings":["Northwestern University, Evanston, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern University, Evanston, IL, USA","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023830568","display_name":"Rafael Molina","orcid":"https://orcid.org/0000-0003-4694-8588"},"institutions":[{"id":"https://openalex.org/I173304897","display_name":"Universidad de Granada","ror":"https://ror.org/04njjy449","country_code":"ES","type":"education","lineage":["https://openalex.org/I173304897"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Rafael Molina","raw_affiliation_strings":["University of Granada, Granada, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Granada, Granada, Spain","institution_ids":["https://openalex.org/I173304897"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048650003","display_name":"Aggelos K. Katsaggelos","orcid":"https://orcid.org/0000-0003-4554-0070"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aggelos K. Katsaggelos","raw_affiliation_strings":["Northwestern University, Evanston, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern University, Evanston, IL, USA","institution_ids":["https://openalex.org/I111979921"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1709,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.50218602,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"700","last_page":"704"},"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9984999895095825,"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":0.993399977684021,"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":0.8464947938919067},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.713038980960846},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.7072044610977173},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6547741889953613},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6099512577056885},{"id":"https://openalex.org/keywords/motion-compensation","display_name":"Motion compensation","score":0.5681530833244324},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5410879850387573},{"id":"https://openalex.org/keywords/compensation","display_name":"Compensation (psychology)","score":0.5141423344612122},{"id":"https://openalex.org/keywords/reuse","display_name":"Reuse","score":0.486013263463974},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4822176694869995},{"id":"https://openalex.org/keywords/temporal-resolution","display_name":"Temporal resolution","score":0.44230902194976807},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.397907555103302},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3358931541442871},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33445507287979126},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06499853730201721}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8464947938919067},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.713038980960846},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.7072044610977173},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6547741889953613},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6099512577056885},{"id":"https://openalex.org/C128840427","wikidata":"https://www.wikidata.org/wiki/Q1302174","display_name":"Motion compensation","level":2,"score":0.5681530833244324},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5410879850387573},{"id":"https://openalex.org/C2780023022","wikidata":"https://www.wikidata.org/wiki/Q1338171","display_name":"Compensation (psychology)","level":2,"score":0.5141423344612122},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.486013263463974},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4822176694869995},{"id":"https://openalex.org/C119666444","wikidata":"https://www.wikidata.org/wiki/Q5977280","display_name":"Temporal resolution","level":2,"score":0.44230902194976807},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.397907555103302},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3358931541442871},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33445507287979126},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06499853730201721},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C548081761","wikidata":"https://www.wikidata.org/wiki/Q180388","display_name":"Waste management","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C11171543","wikidata":"https://www.wikidata.org/wiki/Q41630","display_name":"Psychoanalysis","level":1,"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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/eusipco47968.2020.9287713","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco47968.2020.9287713","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 28th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.5600000023841858,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320324211","display_name":"Ministry of Economy","ror":"https://ror.org/02fn8ac40"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1981990039","https://openalex.org/W2010981316","https://openalex.org/W2044338482","https://openalex.org/W2103346247","https://openalex.org/W2133564696","https://openalex.org/W2138598313","https://openalex.org/W2320725294","https://openalex.org/W2476548250","https://openalex.org/W2557227117","https://openalex.org/W2561585794","https://openalex.org/W2584271190","https://openalex.org/W2601564443","https://openalex.org/W2607041014","https://openalex.org/W2610638008","https://openalex.org/W2781335552","https://openalex.org/W2808109260","https://openalex.org/W2866634454","https://openalex.org/W2962927175","https://openalex.org/W2963372104","https://openalex.org/W2963403868","https://openalex.org/W2964040059","https://openalex.org/W2964121744","https://openalex.org/W2964308564","https://openalex.org/W2966926453","https://openalex.org/W2970781981","https://openalex.org/W2984604230","https://openalex.org/W3101569186","https://openalex.org/W4385245566","https://openalex.org/W6631190155","https://openalex.org/W6679434410","https://openalex.org/W6739901393","https://openalex.org/W6756800942"],"related_works":["https://openalex.org/W3093612317","https://openalex.org/W2613736958","https://openalex.org/W4287776258","https://openalex.org/W3027997911","https://openalex.org/W2175746458","https://openalex.org/W2732542196","https://openalex.org/W2760085659","https://openalex.org/W2883200793","https://openalex.org/W2738221750","https://openalex.org/W3012978760"],"abstract_inverted_index":{"Despite":[0],"the":[1,39,56,60,65,68,98],"success":[2],"of":[3,38,42,100,119],"Recurrent":[4,30,45],"Neural":[5,32],"Networks":[6],"in":[7,15,67,117],"tasks":[8],"involving":[9],"temporal":[10,93,123],"video":[11],"processing,":[12],"few":[13],"works":[14],"Video":[16],"Super-Resolution":[17],"(VSR)":[18],"have":[19],"employed":[20],"them.":[21,79],"In":[22],"this":[23],"work":[24],"we":[25],"propose":[26],"a":[27,43,50,74],"new":[28],"Gated":[29,44],"Convolutional":[31],"Network":[33],"for":[34],"VSR":[35,113],"adapting":[36],"some":[37],"key":[40],"components":[41],"Unit.":[46],"Our":[47],"model":[48,83],"employs":[49],"deformable":[51],"attention":[52],"module":[53],"to":[54,77,84],"align":[55],"features":[57,89],"calculated":[58,88],"at":[59],"previous":[61],"time":[62],"step":[63,70],"with":[64],"ones":[66],"current":[69,112],"and":[71,90,122],"then":[72],"uses":[73],"gated":[75],"operation":[76],"combine":[78],"This":[80],"allows":[81],"our":[82,109],"effectively":[85],"reuse":[86],"previously":[87],"exploit":[91],"longer":[92],"relationships":[94],"between":[95],"frames":[96],"without":[97],"need":[99],"explicit":[101],"motion":[102],"compensation.":[103],"The":[104],"experimental":[105],"validation":[106],"shows":[107],"that":[108],"approach":[110],"outperforms":[111],"learning":[114],"based":[115],"models":[116],"terms":[118],"perceptual":[120],"quality":[121],"consistency.":[124]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
