{"id":"https://openalex.org/W2996756905","doi":"https://doi.org/10.1109/access.2019.2960087","title":"Single-Image Fence Removal Using Deep Convolutional Neural Network","display_name":"Single-Image Fence Removal Using Deep Convolutional Neural Network","publication_year":2019,"publication_date":"2019-12-16","ids":{"openalex":"https://openalex.org/W2996756905","doi":"https://doi.org/10.1109/access.2019.2960087","mag":"2996756905"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2960087","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2960087","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08933392.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08933392.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016311487","display_name":"Takuro Matsui","orcid":"https://orcid.org/0000-0001-9896-7882"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takuro Matsui","raw_affiliation_strings":["Department of Electronics and Electrical Engineering, Keio University, Yokohama-shi, Japan"],"raw_orcid":"https://orcid.org/0000-0001-9896-7882","affiliations":[{"raw_affiliation_string":"Department of Electronics and Electrical Engineering, Keio University, Yokohama-shi, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090181402","display_name":"Masaaki Ikehara","orcid":"https://orcid.org/0000-0003-3461-1507"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masaaki Ikehara","raw_affiliation_strings":["Department of Electronics and Electrical Engineering, Keio University, Yokohama-shi, Japan"],"raw_orcid":"https://orcid.org/0000-0003-3461-1507","affiliations":[{"raw_affiliation_string":"Department of Electronics and Electrical Engineering, Keio University, Yokohama-shi, Japan","institution_ids":["https://openalex.org/I203951103"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I203951103"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.0918,"has_fulltext":true,"cited_by_count":22,"citation_normalized_percentile":{"value":0.82387749,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"8","issue":null,"first_page":"38846","last_page":"38854"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9991000294685364,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9991000294685364,"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":0.998199999332428,"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":0.9973000288009644,"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/fence","display_name":"Fence (mathematics)","score":0.8292536735534668},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7703227400779724},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7612288594245911},{"id":"https://openalex.org/keywords/inpainting","display_name":"Inpainting","score":0.7256613373756409},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7181512117385864},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6252607107162476},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5100255608558655},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.48256129026412964},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.48167359828948975},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4764062166213989},{"id":"https://openalex.org/keywords/fencing","display_name":"Fencing","score":0.4638407826423645},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43945014476776123},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4251112937927246},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1271648108959198}],"concepts":[{"id":"https://openalex.org/C2779652578","wikidata":"https://www.wikidata.org/wiki/Q5442977","display_name":"Fence (mathematics)","level":2,"score":0.8292536735534668},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7703227400779724},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7612288594245911},{"id":"https://openalex.org/C11727466","wikidata":"https://www.wikidata.org/wiki/Q1628157","display_name":"Inpainting","level":3,"score":0.7256613373756409},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7181512117385864},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6252607107162476},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5100255608558655},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.48256129026412964},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.48167359828948975},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4764062166213989},{"id":"https://openalex.org/C48515440","wikidata":"https://www.wikidata.org/wiki/Q5443009","display_name":"Fencing","level":2,"score":0.4638407826423645},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43945014476776123},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4251112937927246},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1271648108959198},{"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/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2960087","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2960087","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08933392.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f7d46f2919394b5d91007e38864aeacc","is_oa":true,"landing_page_url":"https://doaj.org/article/f7d46f2919394b5d91007e38864aeacc","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 38846-38854 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2960087","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2960087","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08933392.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.4000000059604645}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2996756905.pdf","grobid_xml":"https://content.openalex.org/works/W2996756905.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W1521461170","https://openalex.org/W1533861849","https://openalex.org/W1555951418","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1998956470","https://openalex.org/W2022238106","https://openalex.org/W2052094314","https://openalex.org/W2056933304","https://openalex.org/W2105038642","https://openalex.org/W2117519317","https://openalex.org/W2121927366","https://openalex.org/W2131394160","https://openalex.org/W2194775991","https://openalex.org/W2242218935","https://openalex.org/W2288997049","https://openalex.org/W2470163766","https://openalex.org/W2508457857","https://openalex.org/W2509784253","https://openalex.org/W2525054030","https://openalex.org/W2561195635","https://openalex.org/W2810552350","https://openalex.org/W2963881378","https://openalex.org/W2964177954","https://openalex.org/W2964197347","https://openalex.org/W6631943919","https://openalex.org/W6633119130","https://openalex.org/W6639824700","https://openalex.org/W6730269955"],"related_works":["https://openalex.org/W3148571309","https://openalex.org/W1595913894","https://openalex.org/W4281655648","https://openalex.org/W3005416285","https://openalex.org/W1923098019","https://openalex.org/W4390686745","https://openalex.org/W4235284443","https://openalex.org/W2107993728","https://openalex.org/W997989024","https://openalex.org/W2069522681"],"abstract_inverted_index":{"In":[0,78,126],"public":[1],"spaces":[2],"such":[3],"as":[4],"zoos":[5],"and":[6,16,50,92,96,119,138,161,173,183],"sports":[7],"facilities,":[8],"the":[9,51,55,127,166,169],"presence":[10],"of":[11,168,204],"fences":[12],"often":[13],"annoys":[14],"tourists":[15],"professional":[17],"photographers.":[18],"There":[19],"is":[20,40,198],"a":[21,24,29,60,72,113,158,162,181,185,201],"demand":[22],"for":[23,67,171,200],"post-processing":[25],"tool":[26],"to":[27,46,53,99,133],"produce":[28],"non-occluded":[30],"view":[31],"from":[32,90,150],"an":[33],"image":[34,124,187],"or":[35],"video.":[36],"This":[37],"\u201cde-fencing\u201d":[38],"task":[39],"divided":[41],"into":[42],"two":[43],"stages:":[44],"one":[45],"detect":[47,180],"fence":[48,85,94,136,148,182,205],"regions":[49],"other":[52],"fill":[54],"missing":[56],"part.":[57],"For":[58],"over":[59],"decade,":[61],"various":[62],"methods":[63,75,110],"have":[64],"been":[65],"proposed":[66,176],"video-based":[68],"de-fencing.":[69],"However,":[70],"only":[71],"few":[73],"single-image-based":[74],"are":[76,131],"proposed.":[77],"this":[79],"paper,":[80],"we":[81,107,130,145],"focus":[82],"on":[83,112],"single-image":[84],"removal.":[86],"Conventional":[87],"approaches":[88],"suffer":[89],"inaccurate":[91],"non-robust":[93],"detection":[95,172],"inpainting":[97],"due":[98],"less":[100],"content":[101],"information.":[102],"To":[103],"solve":[104],"these":[105],"problems,":[106],"combine":[108],"novel":[109],"based":[111],"deep":[114],"convolutional":[115],"neural":[116],"network":[117],"(CNN)":[118],"classical":[120],"domain":[121],"knowledge":[122],"in":[123],"processing.":[125],"training":[128],"process,":[129],"required":[132],"obtain":[134],"both":[135],"images":[137,149],"corresponding":[139],"non-fence":[140],"ground":[141],"truth":[142],"images.":[143,152,206],"Therefore,":[144],"synthesize":[146],"natural":[147],"real":[151],"Moreover,":[153],"spacial":[154],"filtering":[155],"processing":[156],"(e.g.":[157],"Laplacian":[159],"filter":[160],"Gaussian":[163],"filter)":[164],"improves":[165],"performance":[167],"CNN":[170],"inpainting.":[174],"Our":[175],"method":[177,197],"can":[178],"automatically":[179],"generate":[184],"clean":[186],"without":[188],"any":[189],"user":[190],"input.":[191],"Experimental":[192],"results":[193],"demonstrate":[194],"that":[195],"our":[196],"effective":[199],"broad":[202],"range":[203]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
