{"id":"https://openalex.org/W2784647531","doi":"https://doi.org/10.1109/icpr.2018.8546025","title":"Deep Structured Energy-Based Image Inpainting","display_name":"Deep Structured Energy-Based Image Inpainting","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2784647531","doi":"https://doi.org/10.1109/icpr.2018.8546025","mag":"2784647531"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2018.8546025","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8546025","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1801.07939","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047726401","display_name":"Fazil Altinel","orcid":null},"institutions":[{"id":"https://openalex.org/I201537933","display_name":"Tohoku University","ror":"https://ror.org/01dq60k83","country_code":"JP","type":"education","lineage":["https://openalex.org/I201537933"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Fazil Altinel","raw_affiliation_strings":["Graduate School of Information Sciences, Tohoku University, Sendai, Japan","Graduate School of Information Sciences, Tohoku University, Sendai - Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Information Sciences, Tohoku University, Sendai, Japan","institution_ids":["https://openalex.org/I201537933"]},{"raw_affiliation_string":"Graduate School of Information Sciences, Tohoku University, Sendai - Japan","institution_ids":["https://openalex.org/I201537933"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054882290","display_name":"Mete \u00d6zay","orcid":"https://orcid.org/0000-0002-7189-7260"},"institutions":[{"id":"https://openalex.org/I201537933","display_name":"Tohoku University","ror":"https://ror.org/01dq60k83","country_code":"JP","type":"education","lineage":["https://openalex.org/I201537933"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Mete Ozay","raw_affiliation_strings":["Graduate School of Information Sciences, Tohoku University, Sendai, Japan","Graduate School of Information Sciences, Tohoku University, Sendai - Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of Information Sciences, Tohoku University, Sendai, Japan","institution_ids":["https://openalex.org/I201537933"]},{"raw_affiliation_string":"Graduate School of Information Sciences, Tohoku University, Sendai - Japan","institution_ids":["https://openalex.org/I201537933"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009259465","display_name":"Takayuki Okatani","orcid":"https://orcid.org/0000-0001-9222-763X"},"institutions":[{"id":"https://openalex.org/I4210126580","display_name":"RIKEN Center for Advanced Intelligence Project","ror":"https://ror.org/03ckxwf91","country_code":"JP","type":"facility","lineage":["https://openalex.org/I4210110652","https://openalex.org/I4210126580"]},{"id":"https://openalex.org/I201537933","display_name":"Tohoku University","ror":"https://ror.org/01dq60k83","country_code":"JP","type":"education","lineage":["https://openalex.org/I201537933"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takayuki Okatani","raw_affiliation_strings":["RIKEN Center for AIP, Tokyo, Japan","Graduate School of Information Sciences, Tohoku University, Sendai - Japan"],"affiliations":[{"raw_affiliation_string":"RIKEN Center for AIP, Tokyo, Japan","institution_ids":["https://openalex.org/I4210126580"]},{"raw_affiliation_string":"Graduate School of Information Sciences, Tohoku University, Sendai - Japan","institution_ids":["https://openalex.org/I201537933"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5047726401"],"corresponding_institution_ids":["https://openalex.org/I201537933"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00908749,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"423","last_page":"428"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9994000196456909,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9994000196456909,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9979000091552734,"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":0.9940000176429749,"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/inpainting","display_name":"Inpainting","score":0.8428196907043457},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.803215503692627},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6881899833679199},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6765159368515015},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.6415460109710693},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6399951577186584},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6123965978622437},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.5750331878662109},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5674357414245605},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5656626224517822},{"id":"https://openalex.org/keywords/peak-signal-to-noise-ratio","display_name":"Peak signal-to-noise ratio","score":0.520449161529541},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5072107315063477},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.488309383392334},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42215797305107117},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4213044345378876},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4106295704841614},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19982320070266724},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16712146997451782}],"concepts":[{"id":"https://openalex.org/C11727466","wikidata":"https://www.wikidata.org/wiki/Q1628157","display_name":"Inpainting","level":3,"score":0.8428196907043457},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.803215503692627},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6881899833679199},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6765159368515015},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.6415460109710693},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6399951577186584},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6123965978622437},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.5750331878662109},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5674357414245605},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5656626224517822},{"id":"https://openalex.org/C154579607","wikidata":"https://www.wikidata.org/wiki/Q3373850","display_name":"Peak signal-to-noise ratio","level":3,"score":0.520449161529541},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5072107315063477},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.488309383392334},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42215797305107117},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4213044345378876},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4106295704841614},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19982320070266724},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16712146997451782},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/icpr.2018.8546025","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8546025","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1801.07939","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1801.07939","pdf_url":"https://arxiv.org/pdf/1801.07939","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:2784647531","is_oa":true,"landing_page_url":"https://arxiv.org/abs/1801.07939","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1801.07939","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1801.07939","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1801.07939","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1801.07939","pdf_url":"https://arxiv.org/pdf/1801.07939","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"score":0.9100000262260437,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2784647531.pdf","grobid_xml":"https://content.openalex.org/works/W2784647531.grobid-xml"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1834627138","https://openalex.org/W1850742715","https://openalex.org/W2040370888","https://openalex.org/W2070604790","https://openalex.org/W2099471712","https://openalex.org/W2103560185","https://openalex.org/W2112796928","https://openalex.org/W2173520492","https://openalex.org/W2194775991","https://openalex.org/W2399803916","https://openalex.org/W2557414982","https://openalex.org/W2599275287","https://openalex.org/W2738588019","https://openalex.org/W2739761758","https://openalex.org/W2928160594","https://openalex.org/W2949933669","https://openalex.org/W2953250761","https://openalex.org/W2963266548","https://openalex.org/W2963420272","https://openalex.org/W2963446712","https://openalex.org/W2963789586","https://openalex.org/W2963917315","https://openalex.org/W2964205912","https://openalex.org/W2997095758","https://openalex.org/W6631190155","https://openalex.org/W6639118987","https://openalex.org/W6679687845","https://openalex.org/W6683825394","https://openalex.org/W6685352114","https://openalex.org/W6694605516","https://openalex.org/W6694738858","https://openalex.org/W6703116779","https://openalex.org/W6715998359","https://openalex.org/W6727353649","https://openalex.org/W6727685206","https://openalex.org/W6735184003"],"related_works":["https://openalex.org/W2963899466","https://openalex.org/W2518962550","https://openalex.org/W2605612462","https://openalex.org/W2464212065","https://openalex.org/W3093871422","https://openalex.org/W3210340377","https://openalex.org/W3035718539","https://openalex.org/W1495477614","https://openalex.org/W2899609929","https://openalex.org/W2980585949","https://openalex.org/W2995926841","https://openalex.org/W2277731373","https://openalex.org/W2921234600","https://openalex.org/W2979811391","https://openalex.org/W2171421219","https://openalex.org/W2999520425","https://openalex.org/W2898205719","https://openalex.org/W2972519944","https://openalex.org/W3100037276","https://openalex.org/W2994425563"],"abstract_inverted_index":{"In":[0,15],"this":[1],"paper,":[2],"we":[3,32,103],"propose":[4],"a":[5,53],"structured":[6,36],"image":[7],"inpainting":[8],"method":[9,69],"employing":[10],"an":[11,34,46],"energy":[12,47],"based":[13],"model.":[14],"order":[16],"to":[17,94,109,125],"learn":[18],"structural":[19,40],"relationship":[20,41],"between":[21],"patterns":[22],"observed":[23],"in":[24],"images":[25],"and":[26,117,128],"missing":[27],"regions":[28],"of":[29],"the":[30,72,89,99,114,121,133],"images,":[31],"employ":[33],"energy-based":[35],"prediction":[37],"method.":[38,101],"The":[39,58,137],"is":[42,50,139],"learned":[43],"by":[44,52,98,132],"minimizing":[45],"function":[48],"which":[49,75],"defined":[51],"simple":[54],"convolutional":[55],"neural":[56],"network.":[57],"experimental":[59],"results":[60],"on":[61,88,113,120],"various":[62],"benchmark":[63],"datasets":[64],"show":[65],"that":[66],"our":[67],"proposed":[68],"significantly":[70],"outperforms":[71],"state-of-the-art":[73,100,134],"methods":[74],"use":[76],"Generative":[77],"Adversarial":[78],"Networks":[79],"(GANs).":[80],"We":[81],"obtained":[82,104],"497.35":[83],"mean":[84],"squared":[85],"error":[86],"(MSE)":[87],"Olivetti":[90],"face":[91],"dataset":[92,116],"compared":[93,124],"833.0":[95],"MSE":[96],"provided":[97,131],"Moreover,":[102],"28.4":[105],"dB":[106,119,127],"peak":[107],"signal":[108],"noise":[110],"ratio":[111],"(PSNR)":[112],"SVHN":[115],"23.53":[118],"CelebA":[122],"dataset,":[123],"22.3":[126],"21.3":[129],"dB,":[130],"methods,":[135],"respectively.":[136],"code":[138],"publicly":[140],"available":[141],"<sup":[142],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[143],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[144],".":[145]},"counts_by_year":[],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}