{"id":"https://openalex.org/W2290732660","doi":"https://doi.org/10.1109/apsipa.2015.7415310","title":"Noise bias compensation based on Bayesian inference for tone mapped noisy image","display_name":"Noise bias compensation based on Bayesian inference for tone mapped noisy image","publication_year":2015,"publication_date":"2015-12-01","ids":{"openalex":"https://openalex.org/W2290732660","doi":"https://doi.org/10.1109/apsipa.2015.7415310","mag":"2290732660"},"language":"en","primary_location":{"id":"doi:10.1109/apsipa.2015.7415310","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipa.2015.7415310","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)","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/A5036393534","display_name":"Masahiro Iwahashi","orcid":"https://orcid.org/0000-0002-7566-1247"},"institutions":[{"id":"https://openalex.org/I85922643","display_name":"Nagaoka University of Technology","ror":"https://ror.org/00ys1hz88","country_code":"JP","type":"education","lineage":["https://openalex.org/I85922643"]},{"id":"https://openalex.org/I119806805","display_name":"Nagaoka University","ror":"https://ror.org/02rcadd38","country_code":"JP","type":"education","lineage":["https://openalex.org/I119806805"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Masahiro Iwahashi","raw_affiliation_strings":["Nagaoka University of Technology, Nagaoka, Japan"],"affiliations":[{"raw_affiliation_string":"Nagaoka University of Technology, Nagaoka, Japan","institution_ids":["https://openalex.org/I119806805","https://openalex.org/I85922643"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087504481","display_name":"Fairoza Amira Binti Hamzah","orcid":"https://orcid.org/0000-0002-2554-2709"},"institutions":[{"id":"https://openalex.org/I85922643","display_name":"Nagaoka University of Technology","ror":"https://ror.org/00ys1hz88","country_code":"JP","type":"education","lineage":["https://openalex.org/I85922643"]},{"id":"https://openalex.org/I119806805","display_name":"Nagaoka University","ror":"https://ror.org/02rcadd38","country_code":"JP","type":"education","lineage":["https://openalex.org/I119806805"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Fairoza Amira Binti Hamzah","raw_affiliation_strings":["Nagaoka University of Technology, Nagaoka, Niigata, JP"],"affiliations":[{"raw_affiliation_string":"Nagaoka University of Technology, Nagaoka, Niigata, JP","institution_ids":["https://openalex.org/I119806805","https://openalex.org/I85922643"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091748055","display_name":"Taichi Yoshida","orcid":"https://orcid.org/0000-0002-2463-8231"},"institutions":[{"id":"https://openalex.org/I85922643","display_name":"Nagaoka University of Technology","ror":"https://ror.org/00ys1hz88","country_code":"JP","type":"education","lineage":["https://openalex.org/I85922643"]},{"id":"https://openalex.org/I119806805","display_name":"Nagaoka University","ror":"https://ror.org/02rcadd38","country_code":"JP","type":"education","lineage":["https://openalex.org/I119806805"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Taichi Yoshida","raw_affiliation_strings":["Nagaoka University of Technology, Nagaoka, Japan"],"affiliations":[{"raw_affiliation_string":"Nagaoka University of Technology, Nagaoka, Japan","institution_ids":["https://openalex.org/I119806805","https://openalex.org/I85922643"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015250468","display_name":"Hitoshi Kiya","orcid":"https://orcid.org/0000-0001-8061-3090"},"institutions":[{"id":"https://openalex.org/I69740276","display_name":"Tokyo Metropolitan University","ror":"https://ror.org/00ws30h19","country_code":"JP","type":"education","lineage":["https://openalex.org/I69740276"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hitoshi Kiya","raw_affiliation_strings":["Tokyo Metropolitan University, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Tokyo Metropolitan University, Tokyo, Japan","institution_ids":["https://openalex.org/I69740276"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5036393534"],"corresponding_institution_ids":["https://openalex.org/I119806805","https://openalex.org/I85922643"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.15076547,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"20","issue":null,"first_page":"440","last_page":"443"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9991000294685364,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9983999729156494,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.7002037763595581},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.661927342414856},{"id":"https://openalex.org/keywords/image-noise","display_name":"Image noise","score":0.5708262324333191},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.5406877398490906},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4985332489013672},{"id":"https://openalex.org/keywords/value-noise","display_name":"Value noise","score":0.47741496562957764},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.45053407549858093},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44986438751220703},{"id":"https://openalex.org/keywords/tone-mapping","display_name":"Tone mapping","score":0.44670212268829346},{"id":"https://openalex.org/keywords/gradient-noise","display_name":"Gradient noise","score":0.43729332089424133},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.4343720078468323},{"id":"https://openalex.org/keywords/signal-to-noise-ratio","display_name":"Signal-to-noise ratio (imaging)","score":0.4241913855075836},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.421790212392807},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3993414640426636},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39035555720329285},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3840585947036743},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.2998046278953552},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.26539865136146545},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.21708780527114868},{"id":"https://openalex.org/keywords/noise-floor","display_name":"Noise floor","score":0.1554509699344635}],"concepts":[{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7002037763595581},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.661927342414856},{"id":"https://openalex.org/C35772409","wikidata":"https://www.wikidata.org/wiki/Q1323086","display_name":"Image noise","level":3,"score":0.5708262324333191},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.5406877398490906},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4985332489013672},{"id":"https://openalex.org/C182163834","wikidata":"https://www.wikidata.org/wiki/Q2926529","display_name":"Value noise","level":5,"score":0.47741496562957764},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.45053407549858093},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44986438751220703},{"id":"https://openalex.org/C8641274","wikidata":"https://www.wikidata.org/wiki/Q1030958","display_name":"Tone mapping","level":4,"score":0.44670212268829346},{"id":"https://openalex.org/C200378446","wikidata":"https://www.wikidata.org/wiki/Q4147391","display_name":"Gradient noise","level":5,"score":0.43729332089424133},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.4343720078468323},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.4241913855075836},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.421790212392807},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3993414640426636},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39035555720329285},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3840585947036743},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2998046278953552},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.26539865136146545},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.21708780527114868},{"id":"https://openalex.org/C187612029","wikidata":"https://www.wikidata.org/wiki/Q17083130","display_name":"Noise floor","level":4,"score":0.1554509699344635},{"id":"https://openalex.org/C2780056265","wikidata":"https://www.wikidata.org/wiki/Q106239881","display_name":"High dynamic range","level":3,"score":0.0},{"id":"https://openalex.org/C87133666","wikidata":"https://www.wikidata.org/wiki/Q1161699","display_name":"Dynamic range","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apsipa.2015.7415310","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipa.2015.7415310","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W107968503","https://openalex.org/W1998419211","https://openalex.org/W2025779404","https://openalex.org/W2025813857","https://openalex.org/W2056370875","https://openalex.org/W2058374275","https://openalex.org/W2097073572","https://openalex.org/W2100925004","https://openalex.org/W2108382860","https://openalex.org/W2113824004","https://openalex.org/W2116857329","https://openalex.org/W2140557988","https://openalex.org/W2240434622","https://openalex.org/W2573031522","https://openalex.org/W4238717354","https://openalex.org/W4252813286","https://openalex.org/W7027716524"],"related_works":["https://openalex.org/W2614699099","https://openalex.org/W2955414824","https://openalex.org/W2005333371","https://openalex.org/W2013771251","https://openalex.org/W1969252538","https://openalex.org/W4297491189","https://openalex.org/W2124212511","https://openalex.org/W1964290457","https://openalex.org/W2124371593","https://openalex.org/W2545294132"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"a":[3,8,91,130],"noise":[4,19,24,116,141],"bias":[5,25],"compensation":[6,94],"to":[7,28,77,140],"tone":[9,32],"mapped":[10],"noisy":[11,69],"image":[12,70,111],"so":[13],"that":[14,113,137],"the":[15,18,23,48,53,56,61,68,78,84,104,110,115,125,138,146],"variance":[16],"of":[17,50,55,60,106,114,132],"is":[20,26,101,135,143],"reduced.":[21],"Although":[22],"assumed":[27],"be":[29],"zero":[30],"before":[31,117],"mapping":[33],"(TM),":[34],"it":[35,134],"becomes":[36],"non-zero":[37],"value":[38,86,95],"after":[39],"TM.":[40],"The":[41],"reason":[42],"includes":[43],"some":[44],"factors":[45],"such":[46],"as":[47],"non-linearity":[49],"TM":[51,118],"and":[52,82,112],"asymmetry":[54],"probability":[57],"density":[58],"function":[59],"noise.":[62],"In":[63,97],"this":[64,98],"paper,":[65,99],"pixels":[66],"in":[67,87,109],"are":[71],"classified":[72],"into":[73],"several":[74],"subsets":[75],"according":[76],"observed":[79,136],"pixel":[80,85,107],"value,":[81],"compensates":[83],"each":[88],"subset":[89],"with":[90],"preliminary":[92],"determined":[93,102],"(CV).":[96],"CV":[100],"from":[103],"histogram":[105],"values":[108],"or":[119],"their":[120],"modeled":[121],"versions":[122],"based":[123],"on":[124],"Bayesian":[126],"inference":[127],"deterministically.":[128],"As":[129],"result":[131],"experiments,":[133],"peak-signal":[139],"ratio":[142],"improved":[144],"by":[145],"proposed":[147],"method.":[148]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
