{"id":"https://openalex.org/W2902651335","doi":"https://doi.org/10.1109/icmew.2018.8551569","title":"Video Super Resolution Based on Deep Convolution Neural Network With Two-Stage Motion Compensation","display_name":"Video Super Resolution Based on Deep Convolution Neural Network With Two-Stage Motion Compensation","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2902651335","doi":"https://doi.org/10.1109/icmew.2018.8551569","mag":"2902651335"},"language":"en","primary_location":{"id":"doi:10.1109/icmew.2018.8551569","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2018.8551569","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Multimedia &amp; Expo Workshops (ICMEW)","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/A5101616704","display_name":"Haoyu Ren","orcid":"https://orcid.org/0000-0001-9658-719X"},"institutions":[{"id":"https://openalex.org/I100625452","display_name":"ON Semiconductor (United States)","ror":"https://ror.org/03nw6pt28","country_code":"US","type":"company","lineage":["https://openalex.org/I100625452"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haoyu Ren","raw_affiliation_strings":["Samsung Semiconductor Inc. 4921 Directors Place, San Diego, CA, U.S"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung Semiconductor Inc. 4921 Directors Place, San Diego, CA, U.S","institution_ids":["https://openalex.org/I100625452"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022805456","display_name":"Mostafa El\u2010Khamy","orcid":"https://orcid.org/0000-0001-9421-6037"},"institutions":[{"id":"https://openalex.org/I100625452","display_name":"ON Semiconductor (United States)","ror":"https://ror.org/03nw6pt28","country_code":"US","type":"company","lineage":["https://openalex.org/I100625452"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mostafa El-Khamy","raw_affiliation_strings":["Samsung Semiconductor Inc. 4921 Directors Place, San Diego, CA, U.S"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung Semiconductor Inc. 4921 Directors Place, San Diego, CA, U.S","institution_ids":["https://openalex.org/I100625452"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100331829","display_name":"Jungwon Lee","orcid":"https://orcid.org/0009-0006-6985-2916"},"institutions":[{"id":"https://openalex.org/I100625452","display_name":"ON Semiconductor (United States)","ror":"https://ror.org/03nw6pt28","country_code":"US","type":"company","lineage":["https://openalex.org/I100625452"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jungwon Lee","raw_affiliation_strings":["Samsung Semiconductor Inc. 4921 Directors Place, San Diego, CA, U.S"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung Semiconductor Inc. 4921 Directors Place, San Diego, CA, U.S","institution_ids":["https://openalex.org/I100625452"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I100625452"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":null,"first_page":"1","last_page":"6"},"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":0.9986000061035156,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.9842000007629395,"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/optical-flow","display_name":"Optical flow","score":0.8437367677688599},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7564631700515747},{"id":"https://openalex.org/keywords/motion-compensation","display_name":"Motion compensation","score":0.722217321395874},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.647121012210846},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6085463166236877},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.591902494430542},{"id":"https://openalex.org/keywords/compensation","display_name":"Compensation (psychology)","score":0.5603360533714294},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5180400013923645},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.5064519643783569},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4850563108921051},{"id":"https://openalex.org/keywords/frame-rate","display_name":"Frame rate","score":0.4259885549545288},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.39530354738235474},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2533429265022278},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.12649953365325928}],"concepts":[{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.8437367677688599},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7564631700515747},{"id":"https://openalex.org/C128840427","wikidata":"https://www.wikidata.org/wiki/Q1302174","display_name":"Motion compensation","level":2,"score":0.722217321395874},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.647121012210846},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6085463166236877},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.591902494430542},{"id":"https://openalex.org/C2780023022","wikidata":"https://www.wikidata.org/wiki/Q1338171","display_name":"Compensation (psychology)","level":2,"score":0.5603360533714294},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5180400013923645},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.5064519643783569},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4850563108921051},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.4259885549545288},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39530354738235474},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2533429265022278},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.12649953365325928},{"id":"https://openalex.org/C11171543","wikidata":"https://www.wikidata.org/wiki/Q41630","display_name":"Psychoanalysis","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmew.2018.8551569","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2018.8551569","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Multimedia &amp; Expo Workshops (ICMEW)","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":21,"referenced_works":["https://openalex.org/W1885185971","https://openalex.org/W1913633284","https://openalex.org/W2010981316","https://openalex.org/W2109586214","https://openalex.org/W2117535912","https://openalex.org/W2165939075","https://openalex.org/W2320725294","https://openalex.org/W2476548250","https://openalex.org/W2503339013","https://openalex.org/W2557227117","https://openalex.org/W2605986501","https://openalex.org/W2747675701","https://openalex.org/W2781335552","https://openalex.org/W2949128343","https://openalex.org/W2962777018","https://openalex.org/W2964040059","https://openalex.org/W3106457433","https://openalex.org/W6640021355","https://openalex.org/W6724673846","https://openalex.org/W6747075210","https://openalex.org/W6786191892"],"related_works":["https://openalex.org/W4386083130","https://openalex.org/W3111737715","https://openalex.org/W2117442182","https://openalex.org/W2069571255","https://openalex.org/W3125517176","https://openalex.org/W1975907365","https://openalex.org/W2204864382","https://openalex.org/W2603625296","https://openalex.org/W1909182616","https://openalex.org/W2135082998"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,25],"propose":[4],"methodologies":[5],"to":[6,88,130,147,169],"train":[7],"highly":[8],"accurate":[9],"deep":[10],"convolutional":[11],"neural":[12],"networks":[13,173,177],"(CNNs)":[14],"for":[15,56],"video":[16,28,175],"super":[17,58,113],"resolution":[18,40,67,114],"(SR).":[19],"To":[20],"use":[21],"the":[22,38,46,64,72,81,94,119,132,138,163,185],"inter-frame":[23],"characteristic,":[24],"introduce":[26],"a":[27,53,111],"SR":[29,126,172,176],"network":[30,55,62,115,127],"based":[31,70],"on":[32,71,157],"two-stage":[33],"motion":[34,180],"compensation":[35,181],"(VSR-TMC).":[36],"Firstly,":[37],"low":[39],"(LR)":[41],"frames":[42,69,84,92],"are":[43],"aligned":[44,73],"by":[45,110],"LR":[47,74,139,179],"optical":[48,78,99,120,124,134,140,150],"flow,":[49,141],"and":[50,174],"fed":[51],"into":[52],"3D-convolution":[54],"spatial":[57],"resolution.":[59],"This":[60,97],"3D-conv":[61],"generates":[63],"intermediate":[65,82,106],"high":[66],"(HR)":[68],"frames.":[75,154],"The":[76],"HR":[77,83,91,98,107,133,149,153],"flow":[79,100,121,125,135,151],"between":[80],"is":[85,143,165],"further":[86],"utilized":[87],"refine":[89],"these":[90],"as":[93],"final":[95],"output.":[96],"could":[101],"be":[102],"estimated":[103],"either":[104],"from":[105,137,152],"frames,":[108],"or":[109],"proposed":[112],"specifically":[116],"working":[117],"in":[118],"domain.":[122],"Such":[123],"allows":[128],"us":[129],"get":[131],"directly":[136],"which":[142],"more":[144],"efficient":[145],"compared":[146,168],"calculating":[148],"Experimental":[155],"results":[156],"publicly":[158],"available":[159],"dataset":[160],"demonstrate":[161],"that":[162],"VSR-TMC":[164],"significantly":[166],"better":[167],"single":[170],"image":[171],"with":[178],"only.":[182],"It":[183],"achieves":[184],"state-of-the-art":[186],"performance.":[187]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
