{"id":"https://openalex.org/W2910201959","doi":"https://doi.org/10.1109/euvip.2018.8611710","title":"Blind DCT-based prediction of image denoising efficiency using neural networks","display_name":"Blind DCT-based prediction of image denoising efficiency using neural networks","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2910201959","doi":"https://doi.org/10.1109/euvip.2018.8611710","mag":"2910201959"},"language":"en","primary_location":{"id":"doi:10.1109/euvip.2018.8611710","is_oa":false,"landing_page_url":"https://doi.org/10.1109/euvip.2018.8611710","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 7th European Workshop on Visual Information Processing (EUVIP)","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/A5035624751","display_name":"Oleksii Rubel","orcid":"https://orcid.org/0000-0001-6206-3988"},"institutions":[{"id":"https://openalex.org/I23686167","display_name":"National Aerospace University \u2013 Kharkiv Aviation Institute","ror":"https://ror.org/048j5n646","country_code":"UA","type":"education","lineage":["https://openalex.org/I23686167"]}],"countries":["UA"],"is_corresponding":false,"raw_author_name":"Oleksii Rubel","raw_affiliation_strings":["Department of Information and Communication Technologies, National Aerospace University \u201cKhAI\u201d, Kharkiv, Ukraine","Department of Information and Communication Technologies, National Aerospace University \"KhAI\", Kharkiv, Ukraine"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information and Communication Technologies, National Aerospace University \u201cKhAI\u201d, Kharkiv, Ukraine","institution_ids":["https://openalex.org/I23686167"]},{"raw_affiliation_string":"Department of Information and Communication Technologies, National Aerospace University \"KhAI\", Kharkiv, Ukraine","institution_ids":["https://openalex.org/I23686167"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019970891","display_name":"Andrii Rubel","orcid":"https://orcid.org/0000-0003-0724-6727"},"institutions":[{"id":"https://openalex.org/I23686167","display_name":"National Aerospace University \u2013 Kharkiv Aviation Institute","ror":"https://ror.org/048j5n646","country_code":"UA","type":"education","lineage":["https://openalex.org/I23686167"]}],"countries":["UA"],"is_corresponding":false,"raw_author_name":"Andrii Rubel","raw_affiliation_strings":["Department of Information and Communication Technologies, National Aerospace University \u201cKhAI\u201d, Kharkiv, Ukraine","Department of Information and Communication Technologies, National Aerospace University \"KhAI\", Kharkiv, Ukraine"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information and Communication Technologies, National Aerospace University \u201cKhAI\u201d, Kharkiv, Ukraine","institution_ids":["https://openalex.org/I23686167"]},{"raw_affiliation_string":"Department of Information and Communication Technologies, National Aerospace University \"KhAI\", Kharkiv, Ukraine","institution_ids":["https://openalex.org/I23686167"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011325512","display_name":"Vladimir Lukin","orcid":"https://orcid.org/0000-0002-1443-9685"},"institutions":[{"id":"https://openalex.org/I23686167","display_name":"National Aerospace University \u2013 Kharkiv Aviation Institute","ror":"https://ror.org/048j5n646","country_code":"UA","type":"education","lineage":["https://openalex.org/I23686167"]}],"countries":["UA"],"is_corresponding":false,"raw_author_name":"Vladimir Lukin","raw_affiliation_strings":["Department of Information and Communication Technologies, National Aerospace University \u201cKhAI\u201d, Kharkiv, Ukraine","Department of Information and Communication Technologies, National Aerospace University \"KhAI\", Kharkiv, Ukraine"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information and Communication Technologies, National Aerospace University \u201cKhAI\u201d, Kharkiv, Ukraine","institution_ids":["https://openalex.org/I23686167"]},{"raw_affiliation_string":"Department of Information and Communication Technologies, National Aerospace University \"KhAI\", Kharkiv, Ukraine","institution_ids":["https://openalex.org/I23686167"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090213299","display_name":"Karen Egiazarian","orcid":"https://orcid.org/0000-0002-8135-1085"},"institutions":[{"id":"https://openalex.org/I4210133110","display_name":"Tampere University","ror":null,"country_code":"FI","type":null,"lineage":["https://openalex.org/I4210133110"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Karen Egiazarian","raw_affiliation_strings":["Signal Processing Laboratory, Tampere University of Technology, Tampere, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Signal Processing Laboratory, Tampere University of Technology, Tampere, Finland","institution_ids":["https://openalex.org/I4210133110"]}]}],"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":11,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"19","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9997000098228455,"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":0.9997000098228455,"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/T11165","display_name":"Image and Video Quality Assessment","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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9983000159263611,"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/computer-science","display_name":"Computer science","score":0.7893629670143127},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.7298173904418945},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6711968183517456},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4263458549976349},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42621687054634094},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.42581623792648315},{"id":"https://openalex.org/keywords/non-local-means","display_name":"Non-local means","score":0.423959881067276},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.42215797305107117},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4169802665710449},{"id":"https://openalex.org/keywords/video-denoising","display_name":"Video denoising","score":0.4126822352409363},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40677544474601746},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.27278685569763184},{"id":"https://openalex.org/keywords/image-denoising","display_name":"Image denoising","score":0.19593822956085205}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7893629670143127},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.7298173904418945},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6711968183517456},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4263458549976349},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42621687054634094},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.42581623792648315},{"id":"https://openalex.org/C101453961","wikidata":"https://www.wikidata.org/wiki/Q7048948","display_name":"Non-local means","level":4,"score":0.423959881067276},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.42215797305107117},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4169802665710449},{"id":"https://openalex.org/C30814859","wikidata":"https://www.wikidata.org/wiki/Q4119603","display_name":"Video denoising","level":5,"score":0.4126822352409363},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40677544474601746},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.27278685569763184},{"id":"https://openalex.org/C2983327147","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Image denoising","level":3,"score":0.19593822956085205},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.0},{"id":"https://openalex.org/C23431618","wikidata":"https://www.wikidata.org/wiki/Q1404672","display_name":"Multiview Video Coding","level":4,"score":0.0},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/euvip.2018.8611710","is_oa":false,"landing_page_url":"https://doi.org/10.1109/euvip.2018.8611710","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 7th European Workshop on Visual Information Processing (EUVIP)","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":37,"referenced_works":["https://openalex.org/W1580389772","https://openalex.org/W1964859077","https://openalex.org/W1972006393","https://openalex.org/W1974013408","https://openalex.org/W1985560945","https://openalex.org/W1992869494","https://openalex.org/W2005001434","https://openalex.org/W2015196405","https://openalex.org/W2056370875","https://openalex.org/W2062438821","https://openalex.org/W2067921844","https://openalex.org/W2085518012","https://openalex.org/W2114582993","https://openalex.org/W2115370533","https://openalex.org/W2133665775","https://openalex.org/W2141983208","https://openalex.org/W2148567325","https://openalex.org/W2152059677","https://openalex.org/W2171349048","https://openalex.org/W2190067744","https://openalex.org/W2293068064","https://openalex.org/W2343278664","https://openalex.org/W2404840472","https://openalex.org/W2481798478","https://openalex.org/W2512860429","https://openalex.org/W2576949631","https://openalex.org/W2588281091","https://openalex.org/W2617842429","https://openalex.org/W2622470254","https://openalex.org/W2729302458","https://openalex.org/W2748881860","https://openalex.org/W2964108579","https://openalex.org/W4211253871","https://openalex.org/W4253920039","https://openalex.org/W6677292571","https://openalex.org/W6738991188","https://openalex.org/W6740330286"],"related_works":["https://openalex.org/W233850645","https://openalex.org/W4213271663","https://openalex.org/W2776327255","https://openalex.org/W1974796563","https://openalex.org/W2912885816","https://openalex.org/W2903172302","https://openalex.org/W2078677256","https://openalex.org/W2082304850","https://openalex.org/W4386289761","https://openalex.org/W2065702035"],"abstract_inverted_index":{"Visual":[0],"quality":[1,25,56,139],"of":[2,17,26,50,68,70,73,94,103,145],"digital":[3],"images":[4,51,74],"acquired":[5,27],"by":[6],"modern":[7],"mobile":[8],"cameras":[9],"is":[10,15,159,162,175,204],"crucial":[11],"for":[12,106],"consumers.":[13],"Noise":[14],"one":[16],"the":[18,66,127,136,146,165,186],"factors":[19],"that":[20,164],"can":[21],"significantly":[22],"reduce":[23],"visual":[24,55,138],"data.":[28,62],"There":[29],"are":[30,130,141,169],"many":[31],"image":[32,104],"denoising":[33,44,71,109,151],"methods":[34],"able":[35],"to":[36,59,182,185,191],"efficiently":[37],"suppress":[38],"noise.":[39],"However,":[40],"often":[41],"in":[42,75,126],"practice":[43],"does":[45,88],"not":[46,89],"provide":[47],"sufficient":[48],"enhancement":[49],"or":[52],"even":[53],"demonstrates":[54],"reduction":[57],"compared":[58],"observed":[60],"noisy":[61],"This":[63],"paper":[64],"considers":[65],"problem":[67],"prediction":[69,111,173],"efficiency":[72,110],"a":[76,91,95,100,120,149],"blind":[77],"manner":[78],"under":[79],"additive":[80],"white":[81],"Gaussian":[82],"noise":[83,96],"condition.":[84],"The":[85,108,195],"proposed":[86],"technique":[87],"require":[90],"priori":[92],"knowledge":[93],"variance":[97],"and":[98,135,155,171,177,200],"uses":[99],"moderate":[101],"amount":[102],"data":[105,134],"analysis.":[107],"employs":[112],"neural":[113,167],"networks":[114,168],"(all-to-all":[115],"connected":[116],"multi-layer":[117],"perceptron)to":[118],"create":[119],"regression":[121],"model.":[122],"Image":[123],"statistics":[124],"obtained":[125,166],"spectral":[128],"domain":[129],"used":[131],"as":[132,143],"input":[133],"state-of-the-art":[137],"metrics":[140],"considered":[142],"outputs":[144],"network.":[147],"As":[148],"target":[150],"method,":[152],"block":[153],"matching":[154],"3D":[156],"filtering":[157],"(BM3D)technique":[158],"used.":[160],"It":[161],"demonstrated":[163],"compact":[170],"overall":[172],"procedure":[174],"fast":[176],"has":[178],"an":[179,193],"appropriate":[180],"accuracy":[181],"confidently":[183],"answer":[184],"question:":[187],"\u201cDo":[188],"we":[189],"need":[190],"denoise":[192],"image?\u201d":[194],"full":[196],"dataset,":[197],"executable":[198],"code":[199],"demo":[201],"Android":[202],"application":[203],"available":[205],"at":[206],"https://github.com/asrubel/EUVIP2018.":[207]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":6}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}