{"id":"https://openalex.org/W4385489470","doi":"https://doi.org/10.1145/3594315.3594342","title":"PLNNet: Parameter-learning Neural Network for Global Optimization of Light Field Depth Estimation","display_name":"PLNNet: Parameter-learning Neural Network for Global Optimization of Light Field Depth Estimation","publication_year":2023,"publication_date":"2023-03-17","ids":{"openalex":"https://openalex.org/W4385489470","doi":"https://doi.org/10.1145/3594315.3594342"},"language":"en","primary_location":{"id":"doi:10.1145/3594315.3594342","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3594315.3594342","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 9th International Conference on Computing and Artificial Intelligence","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/A5011452518","display_name":"Yunming Liu","orcid":"https://orcid.org/0000-0001-9923-5691"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yunming Liu","raw_affiliation_strings":["Advanced Network Technology Laboratory, Beijing University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Advanced Network Technology Laboratory, Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007981210","display_name":"Yuxuan Pan","orcid":"https://orcid.org/0000-0002-7762-0713"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxuan Pan","raw_affiliation_strings":["Advanced Network Technology Laboratory, Beijing University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"Advanced Network Technology Laboratory, Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5011452518"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08221666,"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":"1"},"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":0.9986000061035156,"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/T10638","display_name":"Optical measurement and interference techniques","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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6603548526763916},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6560697555541992},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.65085768699646},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5814082622528076},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5529400110244751},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.522839367389679},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5151907205581665},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4660417139530182},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.45094114542007446},{"id":"https://openalex.org/keywords/global-optimization","display_name":"Global optimization","score":0.42238643765449524},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41807660460472107},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.369613915681839},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3677560091018677},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3226483166217804},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2542848587036133},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09411829710006714}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6603548526763916},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6560697555541992},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.65085768699646},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5814082622528076},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5529400110244751},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.522839367389679},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5151907205581665},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4660417139530182},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.45094114542007446},{"id":"https://openalex.org/C164752517","wikidata":"https://www.wikidata.org/wiki/Q5570875","display_name":"Global optimization","level":2,"score":0.42238643765449524},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41807660460472107},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.369613915681839},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3677560091018677},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3226483166217804},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2542848587036133},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09411829710006714}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3594315.3594342","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3594315.3594342","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 9th International Conference on Computing and Artificial Intelligence","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":16,"referenced_works":["https://openalex.org/W2289524987","https://openalex.org/W2329627532","https://openalex.org/W2591697814","https://openalex.org/W2751225115","https://openalex.org/W2752279250","https://openalex.org/W2752921993","https://openalex.org/W2753230437","https://openalex.org/W2890589540","https://openalex.org/W2964178790","https://openalex.org/W2998039742","https://openalex.org/W3202400745","https://openalex.org/W4205138741","https://openalex.org/W4205912819","https://openalex.org/W4224283206","https://openalex.org/W4282826968","https://openalex.org/W4301409532"],"related_works":["https://openalex.org/W1978572805","https://openalex.org/W2383807498","https://openalex.org/W1997992934","https://openalex.org/W1987225439","https://openalex.org/W4238188170","https://openalex.org/W2125114371","https://openalex.org/W2019977573","https://openalex.org/W2149980199","https://openalex.org/W3125766170","https://openalex.org/W2040641410"],"abstract_inverted_index":{"Accurate":[0],"depth":[1,69,102],"estimation":[2],"is":[3,139],"a":[4],"significant":[5],"method":[6],"for":[7,64],"the":[8,31,45,65,72,78,81,93,100,104,109,129],"development":[9],"of":[10,48,68,136],"digital":[11],"wisdom":[12],"medical,":[13],"target":[14],"detection,":[15],"and":[16,26,85,118,131],"3D":[17],"reconstruction.":[18],"However,":[19],"current":[20],"methods":[21],"may":[22],"produce":[23],"noise":[24],"points":[25],"blurry":[27],"edges":[28],"due":[29],"to":[30,39,91,99],"complicated":[32],"texture":[33],"images,":[34],"which":[35],"makes":[36],"it":[37],"essential":[38],"improve":[40],"their":[41],"accuracy":[42],"based":[43],"on":[44,123,128],"abundant":[46],"information":[47],"light":[49],"field":[50],"(LF)":[51],"images.":[52],"In":[53],"this":[54],"paper,":[55],"we":[56],"propose":[57],"an":[58],"end-to-end":[59],"parameter-learning":[60],"neural":[61],"network":[62],"(PLNNet)":[63],"global":[66],"optimization":[67,96],"estimation.":[70],"With":[71],"central":[73],"LF":[74],"sub-aperture":[75],"image":[76],"as":[77],"guide":[79],"image,":[80],"differentiable":[82],"convolutional":[83],"layers":[84],"combined":[86],"loss":[87],"function":[88],"are":[89],"applied":[90],"establish":[92],"edge-preserving":[94],"smoothing":[95],"algorithm.":[97],"Compared":[98],"initial":[101],"maps,":[103],"experimental":[105],"results":[106],"computed":[107],"by":[108],"PLNNet":[110,138],"can":[111],"achieve":[112],"9.13":[113],"MSE(Mean":[114],"Squared":[115],"Error)":[116],"gain":[117,122],"10.46":[119],"BP(Bad":[120],"Pixel)":[121],"average,":[124],"demonstrating":[125],"its":[126],"effectiveness":[127],"edge":[130],"occlusion":[132],"areas.":[133],"The":[134],"implementation":[135],"our":[137],"open-sourced":[140],"at":[141],"https://github.com/lymwxq/PLNNet.":[142]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}