{"id":"https://openalex.org/W2970963559","doi":"https://doi.org/10.1109/icip.2019.8804270","title":"Learning Depth Cues from Focal Stack for Light Field Depth Estimation","display_name":"Learning Depth Cues from Focal Stack for Light Field Depth Estimation","publication_year":2019,"publication_date":"2019-08-26","ids":{"openalex":"https://openalex.org/W2970963559","doi":"https://doi.org/10.1109/icip.2019.8804270","mag":"2970963559"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2019.8804270","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8804270","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","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/A5020050758","display_name":"Wenhui Zhou","orcid":"https://orcid.org/0000-0003-2216-8878"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenhui Zhou","raw_affiliation_strings":["Hangzhou Dianzi University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064059062","display_name":"Enci Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Enci Zhou","raw_affiliation_strings":["Hangzhou Dianzi University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083944786","display_name":"Yuxiang Yan","orcid":"https://orcid.org/0000-0003-2234-1298"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxiang Yan","raw_affiliation_strings":["Hangzhou Dianzi University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067452827","display_name":"Lili Lin","orcid":"https://orcid.org/0000-0001-8196-3244"},"institutions":[{"id":"https://openalex.org/I75059550","display_name":"Zhejiang Gongshang University","ror":"https://ror.org/0569mkk41","country_code":"CN","type":"education","lineage":["https://openalex.org/I75059550"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lili Lin","raw_affiliation_strings":["Zhejiang Gongshang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Zhejiang Gongshang University, Hangzhou, China","institution_ids":["https://openalex.org/I75059550"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074260102","display_name":"Andrew Lumsdaine","orcid":"https://orcid.org/0000-0002-9153-6622"},"institutions":[{"id":"https://openalex.org/I142606810","display_name":"Pacific Northwest National Laboratory","ror":"https://ror.org/05h992307","country_code":"US","type":"facility","lineage":["https://openalex.org/I1325736334","https://openalex.org/I1330989302","https://openalex.org/I142606810","https://openalex.org/I39565521"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andrew Lumsdaine","raw_affiliation_strings":["Pacific Northwest Laboratory, Richland, WA, USA"],"affiliations":[{"raw_affiliation_string":"Pacific Northwest Laboratory, Richland, WA, USA","institution_ids":["https://openalex.org/I142606810"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5020050758"],"corresponding_institution_ids":["https://openalex.org/I50760025"],"apc_list":null,"apc_paid":null,"fwci":1.5183,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.86456137,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1074","last_page":"1078"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","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/T10531","display_name":"Advanced Vision and Imaging","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/T11019","display_name":"Image Enhancement Techniques","score":0.9970999956130981,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental 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":0.7389732599258423},{"id":"https://openalex.org/keywords/light-field","display_name":"Light field","score":0.7330635190010071},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7101348638534546},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6218997836112976},{"id":"https://openalex.org/keywords/epipolar-geometry","display_name":"Epipolar geometry","score":0.6202829480171204},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6027013659477234},{"id":"https://openalex.org/keywords/stack","display_name":"Stack (abstract data type)","score":0.5828787684440613},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5357064008712769},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5296558737754822},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4884449541568756},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48634201288223267},{"id":"https://openalex.org/keywords/cardinal-point","display_name":"Cardinal point","score":0.46770307421684265},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4619951546192169},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43849191069602966},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.41579437255859375},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34838682413101196},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.22332096099853516},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18394151329994202},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.14646703004837036}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7389732599258423},{"id":"https://openalex.org/C48983235","wikidata":"https://www.wikidata.org/wiki/Q593161","display_name":"Light field","level":2,"score":0.7330635190010071},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7101348638534546},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6218997836112976},{"id":"https://openalex.org/C23379248","wikidata":"https://www.wikidata.org/wiki/Q200904","display_name":"Epipolar geometry","level":3,"score":0.6202829480171204},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6027013659477234},{"id":"https://openalex.org/C9395851","wikidata":"https://www.wikidata.org/wiki/Q177929","display_name":"Stack (abstract data type)","level":2,"score":0.5828787684440613},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5357064008712769},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5296558737754822},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4884449541568756},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48634201288223267},{"id":"https://openalex.org/C138395690","wikidata":"https://www.wikidata.org/wiki/Q376733","display_name":"Cardinal point","level":2,"score":0.46770307421684265},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4619951546192169},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43849191069602966},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41579437255859375},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34838682413101196},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.22332096099853516},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18394151329994202},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.14646703004837036},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2019.8804270","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8804270","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320338464","display_name":"Natural Science Foundation of Zhejiang Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1585668122","https://openalex.org/W1836465849","https://openalex.org/W1914596733","https://openalex.org/W2070342576","https://openalex.org/W2097117768","https://openalex.org/W2137313943","https://openalex.org/W2147872462","https://openalex.org/W2149694199","https://openalex.org/W2211024058","https://openalex.org/W2289524987","https://openalex.org/W2296677960","https://openalex.org/W2337243791","https://openalex.org/W2464308330","https://openalex.org/W2549094858","https://openalex.org/W2591697814","https://openalex.org/W2593294845","https://openalex.org/W2593564043","https://openalex.org/W2612835556","https://openalex.org/W2737842117","https://openalex.org/W2741694910","https://openalex.org/W2751225115","https://openalex.org/W2752279250","https://openalex.org/W2766499215","https://openalex.org/W2778641202","https://openalex.org/W2783516823","https://openalex.org/W2798487245","https://openalex.org/W2884366600","https://openalex.org/W2902024778","https://openalex.org/W2949117887","https://openalex.org/W2964178790","https://openalex.org/W6634891648","https://openalex.org/W6638667902","https://openalex.org/W6668193680","https://openalex.org/W6680152570","https://openalex.org/W6703980765","https://openalex.org/W6729480787","https://openalex.org/W6734178593","https://openalex.org/W6734447043","https://openalex.org/W6747619265"],"related_works":["https://openalex.org/W4251504644","https://openalex.org/W132764016","https://openalex.org/W2148054235","https://openalex.org/W2132043085","https://openalex.org/W4385805064","https://openalex.org/W2929681741","https://openalex.org/W2594336302","https://openalex.org/W2787003719","https://openalex.org/W3209881651","https://openalex.org/W2619811041"],"abstract_inverted_index":{"Deep":[0],"neural":[1,124],"networks":[2,125],"have":[3],"shown":[4],"their":[5],"excellent":[6],"abilities":[7],"in":[8],"light":[9,33,53,76,143],"field":[10,77,144],"depth":[11,21,64,78],"estimation.":[12,79],"Most":[13],"of":[14,32,47,52,111,132,162,168],"learning":[15],"based":[16],"approaches":[17],"focus":[18],"on":[19,139,159],"the":[20,25,40,48,72,83,93,129,140,156,160],"feature":[22],"extraction":[23],"from":[24,71],"epipolar":[26],"plane":[27],"images":[28],"(EPIs)":[29],"or":[30],"sub-apertures":[31],"field,":[34],"while":[35],"pay":[36],"less":[37],"attention":[38],"to":[39,127],"focal":[41,73,105,112],"stack":[42,74,106,113],"which":[43,62],"is":[44],"also":[45],"one":[46],"most":[49],"distinctive":[50],"characteristics":[51],"field.":[54],"In":[55],"this":[56],"paper,":[57],"we":[58,81,101,119],"propose":[59],"a":[60,87,103,109,121],"FocalStackNet":[61],"learns":[63],"semantic":[65],"features":[66],"and":[67,91,107,164],"local":[68],"structure":[69],"information":[70],"for":[75],"Specifically,":[80],"formulate":[82],"disparity":[84,95,130],"estimation":[85],"as":[86,115],"pixel-wise":[88],"classification":[89],"task,":[90],"discretize":[92],"continuous":[94],"range":[96],"into":[97],"115":[98],"bins.":[99],"Then":[100],"generate":[102],"discrete":[104],"extract":[108],"set":[110],"patches":[114],"training":[116],"data.":[117],"Finally,":[118],"train":[120],"two-pathway":[122],"convolutional":[123],"(CNN)":[126],"predict":[128],"label":[131],"each":[133],"pixel.":[134],"Evaluation":[135],"experiments":[136],"are":[137],"carried":[138],"public":[141],"4D":[142],"synthetic":[145],"dataset.":[146],"Our":[147],"method":[148],"achieves":[149],"state-of-the-art":[150],"performance.":[151],"It":[152],"ranks":[153],"first":[154],"among":[155],"published":[157],"methods":[158],"aspects":[161],"average":[163],"median":[165],"error":[166],"scores":[167],"Bad":[169],"Pixel":[170],"Ratio":[171],"0.03.":[172]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}