{"id":"https://openalex.org/W4416251666","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228200","title":"Adaptive-Weighted Convolutional Filters: A Depth Discrepancy-Focused Approach to Defocus Estimation","display_name":"Adaptive-Weighted Convolutional Filters: A Depth Discrepancy-Focused Approach to Defocus Estimation","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251666","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228200"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228200","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228200","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5120403241","display_name":"Yan Siwei","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yan Siwei","raw_affiliation_strings":["Sichuan University,Dept. National Key Laboratory of Fundamental Science on Synthetic Vision,Chengdu,China"],"affiliations":[{"raw_affiliation_string":"Sichuan University,Dept. National Key Laboratory of Fundamental Science on Synthetic Vision,Chengdu,China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038618652","display_name":"Jiangping Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210125143","display_name":"Chengdu University","ror":"https://ror.org/034z67559","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210125143"]},{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhu Jiangping","raw_affiliation_strings":["Sichuan University,Dept. College of Computer Science,Chengdu,China"],"affiliations":[{"raw_affiliation_string":"Sichuan University,Dept. College of Computer Science,Chengdu,China","institution_ids":["https://openalex.org/I4210125143","https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101509972","display_name":"Pei Zhou","orcid":"https://orcid.org/0000-0003-1811-2548"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhou Pei","raw_affiliation_strings":["Sichuan University,Dept. National Key Laboratory of Fundamental Science on Synthetic Vision,Chengdu,China"],"affiliations":[{"raw_affiliation_string":"Sichuan University,Dept. National Key Laboratory of Fundamental Science on Synthetic Vision,Chengdu,China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113243091","display_name":"Du Junlin","orcid":null},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]},{"id":"https://openalex.org/I4210125143","display_name":"Chengdu University","ror":"https://ror.org/034z67559","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210125143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Du Junlin","raw_affiliation_strings":["Sichuan University,Dept. College of Computer Science,Chengdu,China"],"affiliations":[{"raw_affiliation_string":"Sichuan University,Dept. College of Computer Science,Chengdu,China","institution_ids":["https://openalex.org/I4210125143","https://openalex.org/I24185976"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5120403241"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.46010536,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9476000070571899,"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":0.9476000070571899,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.021800000220537186,"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":0.01759999990463257,"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/pixel","display_name":"Pixel","score":0.7149999737739563},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6675999760627747},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6205999851226807},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5874999761581421},{"id":"https://openalex.org/keywords/depth-map","display_name":"Depth map","score":0.5231999754905701},{"id":"https://openalex.org/keywords/measured-depth","display_name":"Measured depth","score":0.4632999897003174},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4377000033855438}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7407000064849854},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7149999737739563},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6771000027656555},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6711000204086304},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6675999760627747},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6205999851226807},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5874999761581421},{"id":"https://openalex.org/C141268832","wikidata":"https://www.wikidata.org/wiki/Q2940499","display_name":"Depth map","level":3,"score":0.5231999754905701},{"id":"https://openalex.org/C113346285","wikidata":"https://www.wikidata.org/wiki/Q6804193","display_name":"Measured depth","level":2,"score":0.4632999897003174},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4377000033855438},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.41839998960494995},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.412200003862381},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3781999945640564},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.3528999984264374},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.3278000056743622},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.3147999942302704},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3059999942779541},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2863999903202057},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26589998602867126},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228200","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228200","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1787360621","https://openalex.org/W1965357857","https://openalex.org/W1978040550","https://openalex.org/W2565061173","https://openalex.org/W2601564443","https://openalex.org/W2610447292","https://openalex.org/W2737368828","https://openalex.org/W2790594255","https://openalex.org/W2886322387","https://openalex.org/W2897503218","https://openalex.org/W2961343177","https://openalex.org/W2965533397","https://openalex.org/W2979361430","https://openalex.org/W2980467688","https://openalex.org/W3006617518","https://openalex.org/W3008252701","https://openalex.org/W3034901835","https://openalex.org/W3039325245","https://openalex.org/W3047791393","https://openalex.org/W3107360833","https://openalex.org/W3135128126","https://openalex.org/W3156000195","https://openalex.org/W3173274332","https://openalex.org/W4210554217","https://openalex.org/W4214597897","https://openalex.org/W4229862590","https://openalex.org/W4234282793","https://openalex.org/W4312515828","https://openalex.org/W4312522806","https://openalex.org/W4327813582","https://openalex.org/W4386065543"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,25,108,118,176],"rapid":[2],"development":[3],"of":[4,120],"deep":[5],"learning,":[6],"convolutional":[7,60],"neural":[8],"network":[9],"(CNN)-based":[10],"defocus":[11,121,179],"depth":[12,27,43,75,104,113,125,166,180,193],"recovery":[13,126,181],"techniques":[14],"have":[15],"gained":[16],"significant":[17],"attention.":[18],"However,":[19],"a":[20,150,184],"critical":[21],"issue":[22],"observed":[23],"in":[24,45,52,169,178],"recovered":[26],"maps":[28],"is":[29,131],"edge":[30,83],"blurring.":[31],"This":[32,173],"problem":[33],"arises":[34],"because":[35],"standard":[36],"convolution":[37,93,139],"operations":[38],"fail":[39],"to":[40,78,134],"account":[41],"for":[42,187],"variations":[44],"3D":[46,138],"space,":[47],"treating":[48],"physically":[49],"adjacent":[50],"pixels":[51,73,111],"2D":[53,136],"images":[54],"as":[55,149],"having":[56],"consistent":[57],"depth.":[58],"As":[59],"kernels":[61],"extract":[62],"features":[63],"within":[64],"their":[65],"receptive":[66],"fields,":[67],"they":[68],"inadvertently":[69],"introduce":[70],"correlations":[71],"between":[72],"with":[74,112],"disparities,":[76,114],"leading":[77],"blurred":[79],"regions":[80],"and":[81,123,137,141,163,182,195],"degraded":[82],"quality.":[84],"To":[85],"address":[86],"this":[87],"limitation,":[88],"we":[89],"propose":[90],"an":[91],"adaptive":[92],"(AdpConv)":[94],"mechanism":[95],"that":[96,156],"dynamically":[97],"adjusts":[98],"kernel":[99],"weights":[100],"based":[101],"on":[102],"local":[103],"discrepancies.":[105],"By":[106],"minimizing":[107],"interference":[109],"from":[110],"our":[115,157],"method":[116,158],"enhances":[117],"accuracy":[119],"measurement":[122],"improves":[124,165],"performance.":[127],"The":[128],"proposed":[129],"approach":[130],"generalizable,":[132],"applicable":[133],"both":[135],"operations,":[140],"can":[142],"be":[143],"seamlessly":[144],"integrated":[145],"into":[146],"existing":[147],"models":[148],"plug-and-play":[151],"component.":[152],"Extensive":[153],"experiments":[154],"demonstrate":[155],"achieves":[159],"superior":[160],"quantitative":[161],"metrics":[162],"significantly":[164],"prediction,":[167],"particularly":[168],"challenging":[170],"textured":[171],"regions.":[172],"work":[174],"advances":[175],"state-of-the-art":[177],"offers":[183],"versatile":[185],"solution":[186],"related":[188],"computer":[189],"vision":[190],"tasks":[191],"involving":[192],"estimation":[194],"image":[196],"restoration.":[197]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-14T00:00:00"}