{"id":"https://openalex.org/W2971276362","doi":"https://doi.org/10.1109/icip.2019.8803612","title":"Blurring-Effect-Free CNN for Optimization of Structural Edges in Focus Stacking","display_name":"Blurring-Effect-Free CNN for Optimization of Structural Edges in Focus Stacking","publication_year":2019,"publication_date":"2019-08-26","ids":{"openalex":"https://openalex.org/W2971276362","doi":"https://doi.org/10.1109/icip.2019.8803612","mag":"2971276362"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2019.8803612","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8803612","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":"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/A5100451406","display_name":"Wentao Li","orcid":"https://orcid.org/0000-0002-5367-3482"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wentao Li","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045183950","display_name":"Guijin Wang","orcid":"https://orcid.org/0000-0002-2131-3044"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guijin Wang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006817088","display_name":"Xinghao Chen","orcid":"https://orcid.org/0000-0002-2102-8235"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinghao Chen","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083479069","display_name":"Xuanwu Yin","orcid":null},"institutions":[{"id":"https://openalex.org/I2802592688","display_name":"Kirin (Japan)","ror":"https://ror.org/0586k5242","country_code":"JP","type":"company","lineage":["https://openalex.org/I2802592688"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Xuanwu Yin","raw_affiliation_strings":["Dept. of Kirin Chipset and Technology Development, Hisilicon"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Kirin Chipset and Technology Development, Hisilicon","institution_ids":["https://openalex.org/I2802592688"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101588221","display_name":"Xiaowei Hu","orcid":"https://orcid.org/0000-0002-5894-2127"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaowei Hu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4634","last_page":"4638"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9977999925613403,"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":0.9962000250816345,"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/focus","display_name":"Focus (optics)","score":0.8796679973602295},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7626633644104004},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.7572188377380371},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7370527982711792},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6870132684707642},{"id":"https://openalex.org/keywords/stacking","display_name":"Stacking","score":0.6546390652656555},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6387813091278076},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5302585959434509},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.467842698097229},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3491315245628357},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.08426204323768616}],"concepts":[{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.8796679973602295},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7626633644104004},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7572188377380371},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7370527982711792},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6870132684707642},{"id":"https://openalex.org/C33347731","wikidata":"https://www.wikidata.org/wiki/Q285210","display_name":"Stacking","level":2,"score":0.6546390652656555},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6387813091278076},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5302585959434509},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.467842698097229},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3491315245628357},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.08426204323768616},{"id":"https://openalex.org/C46141821","wikidata":"https://www.wikidata.org/wiki/Q209402","display_name":"Nuclear magnetic resonance","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2019.8803612","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8803612","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":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1618516051","https://openalex.org/W1923779427","https://openalex.org/W1985231173","https://openalex.org/W2039608780","https://openalex.org/W2059535870","https://openalex.org/W2062944056","https://openalex.org/W2128511678","https://openalex.org/W2469546413","https://openalex.org/W2559870345","https://openalex.org/W2591697814","https://openalex.org/W2742180631","https://openalex.org/W2766445487","https://openalex.org/W2777967210","https://openalex.org/W2898215776","https://openalex.org/W3101550650","https://openalex.org/W4245960003","https://openalex.org/W6734447043"],"related_works":["https://openalex.org/W2035329725","https://openalex.org/W4376641153","https://openalex.org/W2070875936","https://openalex.org/W4250391473","https://openalex.org/W3045075405","https://openalex.org/W4302292679","https://openalex.org/W4241625287","https://openalex.org/W2050788868","https://openalex.org/W4295885776","https://openalex.org/W3206716821"],"abstract_inverted_index":{"Focus":[0],"stacking":[1,26],"is":[2,93],"a":[3,67],"computational":[4],"technique":[5],"to":[6,73,80,97,100],"extend":[7],"the":[8,30,51,87,94,116],"Depth":[9],"of":[10,83,89,107],"Field":[11],"(DOF)":[12],"through":[13],"combining":[14],"multiple":[15],"images":[16,104],"taken":[17],"at":[18],"various":[19],"focus":[20,25],"distances.":[21],"In":[22,62],"depth-estimation":[23],"based":[24,40],"approaches,":[27],"researchers":[28],"reconstruct":[29,74],"all-in-focus":[31,84,102,122],"image":[32],"by":[33],"extracting":[34],"pixels":[35],"from":[36,77,110],"focal":[37,78],"stack":[38,79],"pixel-by-pixel":[39],"on":[41],"estimated":[42],"depthmap.":[43,111],"However,":[44],"existing":[45],"methods":[46],"could":[47,119],"not":[48],"cope":[49],"with":[50],"blurring-effect":[52],"problem":[53],"in":[54],"structural":[55],"edges,":[56],"where":[57],"depth":[58],"values":[59],"change":[60],"abruptly.":[61],"this":[63],"work,":[64],"we":[65],"propose":[66],"novel":[68],"convolutional":[69],"neural":[70],"network":[71],"(BEF-CNN)":[72],"blurring-effect-free":[75],"patches":[76],"enhance":[81],"performance":[82],"image.":[85],"To":[86],"best":[88,121],"our":[90],"knowledge,":[91],"it":[92],"first":[95],"work":[96],"utilize":[98],"CNN":[99],"generate":[101],"RGB":[103],"directly":[105],"instead":[106],"pixel-to-pixel":[108],"correspondence":[109],"Experimental":[112],"results":[113],"validate":[114],"that":[115],"proposed":[117],"algorithm":[118],"achieve":[120],"reconstruction":[123],"performance.":[124]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}