{"id":"https://openalex.org/W2921355301","doi":"https://doi.org/10.23919/apsipa.2018.8659491","title":"High Dynamic Range Image Generation Based on Convolutional Weight Optimization Robust to Mixed Noise Removal","display_name":"High Dynamic Range Image Generation Based on Convolutional Weight Optimization Robust to Mixed Noise Removal","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2921355301","doi":"https://doi.org/10.23919/apsipa.2018.8659491","mag":"2921355301"},"language":"en","primary_location":{"id":"doi:10.23919/apsipa.2018.8659491","is_oa":false,"landing_page_url":"https://doi.org/10.23919/apsipa.2018.8659491","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","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/A5101483576","display_name":"Ryo Matsuoka","orcid":"https://orcid.org/0000-0003-4774-1183"},"institutions":[{"id":"https://openalex.org/I201933988","display_name":"Kagawa University","ror":"https://ror.org/04j7mzp05","country_code":"JP","type":"education","lineage":["https://openalex.org/I201933988"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Ryo Matsuoka","raw_affiliation_strings":["Kagawa University, Kagawa, Japan"],"affiliations":[{"raw_affiliation_string":"Kagawa University, Kagawa, Japan","institution_ids":["https://openalex.org/I201933988"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040341698","display_name":"Shunsuke Ono","orcid":"https://orcid.org/0000-0001-7890-5131"},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shunsuke Ono","raw_affiliation_strings":["Tokyo Institute of Technology, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Tokyo Institute of Technology, Tokyo, Japan","institution_ids":["https://openalex.org/I114531698"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025207272","display_name":"Masahiro Okuda","orcid":"https://orcid.org/0000-0002-3245-2672"},"institutions":[{"id":"https://openalex.org/I17056963","display_name":"The University of Kitakyushu","ror":"https://ror.org/03mfefw72","country_code":"JP","type":"education","lineage":["https://openalex.org/I17056963"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masahiro Okuda","raw_affiliation_strings":["The University of Kitakyushu, Fukuoka, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Kitakyushu, Fukuoka, Japan","institution_ids":["https://openalex.org/I17056963"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101483576"],"corresponding_institution_ids":["https://openalex.org/I201933988"],"apc_list":null,"apc_paid":null,"fwci":0.1062,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.50227642,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1066","last_page":"1070"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","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/T10688","display_name":"Image and Signal Denoising Methods","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":0.9998999834060669,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9994999766349792,"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":0.6834518909454346},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6506769061088562},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6334071159362793},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5868303775787354},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5409726500511169},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5374048352241516},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5343368053436279},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5200855135917664},{"id":"https://openalex.org/keywords/high-dynamic-range","display_name":"High dynamic range","score":0.4865838289260864},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4852449893951416},{"id":"https://openalex.org/keywords/weight-function","display_name":"Weight function","score":0.4812169373035431},{"id":"https://openalex.org/keywords/high-dynamic-range-imaging","display_name":"High-dynamic-range imaging","score":0.4694459140300751},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4170774221420288},{"id":"https://openalex.org/keywords/dynamic-range","display_name":"Dynamic range","score":0.37188905477523804},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2937779426574707},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07629063725471497}],"concepts":[{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6834518909454346},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6506769061088562},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6334071159362793},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5868303775787354},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5409726500511169},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5374048352241516},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5343368053436279},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5200855135917664},{"id":"https://openalex.org/C2780056265","wikidata":"https://www.wikidata.org/wiki/Q106239881","display_name":"High dynamic range","level":3,"score":0.4865838289260864},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4852449893951416},{"id":"https://openalex.org/C134466208","wikidata":"https://www.wikidata.org/wiki/Q1520657","display_name":"Weight function","level":2,"score":0.4812169373035431},{"id":"https://openalex.org/C2781399445","wikidata":"https://www.wikidata.org/wiki/Q309254","display_name":"High-dynamic-range imaging","level":4,"score":0.4694459140300751},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4170774221420288},{"id":"https://openalex.org/C87133666","wikidata":"https://www.wikidata.org/wiki/Q1161699","display_name":"Dynamic range","level":2,"score":0.37188905477523804},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2937779426574707},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07629063725471497},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.23919/apsipa.2018.8659491","is_oa":false,"landing_page_url":"https://doi.org/10.23919/apsipa.2018.8659491","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"},{"id":"pmh:oai:t2r2.star.titech.ac.jp:50438364","is_oa":false,"landing_page_url":"http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100778364","pdf_url":null,"source":{"id":"https://openalex.org/S4377196385","display_name":"Tokyo Tech Research Repository (Tokyo Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I114531698","host_organization_name":"Tokyo Institute of Technology","host_organization_lineage":["https://openalex.org/I114531698"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1470949719","https://openalex.org/W1667895010","https://openalex.org/W1670547151","https://openalex.org/W1980225679","https://openalex.org/W1998991750","https://openalex.org/W2005089986","https://openalex.org/W2014823423","https://openalex.org/W2027150759","https://openalex.org/W2035229182","https://openalex.org/W2046033161","https://openalex.org/W2053655320","https://openalex.org/W2056370875","https://openalex.org/W2069281566","https://openalex.org/W2075041908","https://openalex.org/W2082473969","https://openalex.org/W2130700878","https://openalex.org/W2136961432","https://openalex.org/W2236181121","https://openalex.org/W2240434622","https://openalex.org/W2295545172","https://openalex.org/W2336176486","https://openalex.org/W2418010766","https://openalex.org/W2540117966","https://openalex.org/W2552290192","https://openalex.org/W2999388423","https://openalex.org/W3100489614","https://openalex.org/W4236229596","https://openalex.org/W6728795657"],"related_works":["https://openalex.org/W2152030049","https://openalex.org/W2884377208","https://openalex.org/W2014689642","https://openalex.org/W2036724498","https://openalex.org/W3123618386","https://openalex.org/W2410869481","https://openalex.org/W2272915279","https://openalex.org/W2197132757","https://openalex.org/W1939253538","https://openalex.org/W2162796060"],"abstract_inverted_index":{"Exposure":[0],"blending":[1,43,67,81,142],"generates":[2,91],"a":[3,17,53,60,77,92,112,121,126,138,175],"high":[4],"dynamic":[5,36],"range":[6],"(HDR)":[7],"image":[8,108],"from":[9],"multiple":[10],"exposure":[11,66],"images.":[12,56],"However,":[13],"when":[14],"photographing":[15],"in":[16,49,130,141],"low-light":[18],"scene,":[19],"these":[20],"images":[21,168],"are":[22],"deteriorated":[23],"due":[24],"to":[25,102,143,148],"various":[26],"types":[27],"of":[28,52,98,107,114,164,184],"noise,":[29,47],"and":[30,73,90,150],"then":[31],"it":[32],"brings":[33],"down":[34],"the":[35,50,84,104,145,152,157,165,170,182,185],"range.":[37],"Existing":[38],"methods":[39],"based":[40],"on":[41],"pixel-wise":[42],"cannot":[44],"sufficiently":[45],"reduce":[46],"especially":[48],"case":[51],"few":[54,78],"input":[55,167],"This":[57],"report":[58],"proposes":[59],"convolutional":[61],"weight":[62,115,122,172],"optimization":[63,123,128,153],"method":[64,86,146,187],"for":[65,117],"that":[68],"robustly":[69],"removes":[70],"mixed":[71],"noise":[72],"under/over-exposed":[74],"pixels":[75,89],"with":[76,169,189],"inputs.":[79],"In":[80],"each":[82],"pixel,":[83],"proposed":[85,186],"convolves":[87],"neighboring":[88],"noise-free":[93,176],"HDR":[94,177],"image.":[95,178],"The":[96,161],"convolution":[97],"local":[99],"regions":[100],"enables":[101],"enhance":[103],"denoising":[105],"capability":[106],"blending.":[109],"To":[110],"find":[111],"set":[113],"maps":[116,173],"convolution,":[118],"we":[119],"introduce":[120],"problem":[124,154],"as":[125,137],"convex":[127],"problem,":[129],"which":[131],"Huber":[132],"loss":[133],"function":[134],"is":[135],"utilized":[136],"fidelity":[139],"measure":[140],"make":[144],"robust":[147],"outliers,":[149],"solve":[151],"by":[155],"using":[156],"primal-dual":[158],"splitting":[159],"method.":[160],"weighted":[162],"sum":[163],"noisy":[166],"estimated":[171],"makes":[174],"Experimental":[179],"results":[180],"show":[181],"validity":[183],"compared":[188],"several":[190],"conventional":[191],"methods.":[192]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}