{"id":"https://openalex.org/W3036228630","doi":"https://doi.org/10.1109/lsp.2020.3002652","title":"Learning Blind Pixelwise Affine Image Denoiser With Single Noisy Images","display_name":"Learning Blind Pixelwise Affine Image Denoiser With Single Noisy Images","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3036228630","doi":"https://doi.org/10.1109/lsp.2020.3002652","mag":"3036228630"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2020.3002652","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2020.3002652","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-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/A5003535222","display_name":"Jaeseok Byun","orcid":"https://orcid.org/0000-0002-1941-0822"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jaeseok Byun","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-1941-0822","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080346989","display_name":"Taesup Moon","orcid":"https://orcid.org/0000-0002-9257-6503"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Taesup Moon","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-9257-6503","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea","institution_ids":["https://openalex.org/I848706"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I848706"],"apc_list":null,"apc_paid":null,"fwci":0.575,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.68209197,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"27","issue":null,"first_page":"1105","last_page":"1109"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":1.0,"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":1.0,"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":0.9988999962806702,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/noise-reduction","display_name":"Noise reduction","score":0.720770001411438},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.6993861198425293},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6876932382583618},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6436663866043091},{"id":"https://openalex.org/keywords/affine-transformation","display_name":"Affine transformation","score":0.6309676170349121},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5881364345550537},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5745429992675781},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5697072148323059},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.5630286335945129},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.5275332927703857},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5061447024345398},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4529898464679718},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3884725868701935},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.3163839280605316},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25385144352912903},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.21878769993782043},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07624366879463196},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.06439527869224548}],"concepts":[{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.720770001411438},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.6993861198425293},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6876932382583618},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6436663866043091},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.6309676170349121},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5881364345550537},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5745429992675781},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5697072148323059},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.5630286335945129},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.5275332927703857},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5061447024345398},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4529898464679718},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3884725868701935},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.3163839280605316},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25385144352912903},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.21878769993782043},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07624366879463196},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.06439527869224548},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2020.3002652","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2020.3002652","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W2006221457","https://openalex.org/W2048695508","https://openalex.org/W2052452674","https://openalex.org/W2056370875","https://openalex.org/W2097073572","https://openalex.org/W2121927366","https://openalex.org/W2136035751","https://openalex.org/W2160547390","https://openalex.org/W2172275395","https://openalex.org/W2508457857","https://openalex.org/W2520164769","https://openalex.org/W2764207251","https://openalex.org/W2793146153","https://openalex.org/W2799192307","https://openalex.org/W2799502373","https://openalex.org/W2902857081","https://openalex.org/W2914204228","https://openalex.org/W2949725501","https://openalex.org/W2952323569","https://openalex.org/W2962767526","https://openalex.org/W2963085671","https://openalex.org/W2963571608","https://openalex.org/W2963725279","https://openalex.org/W2964046669","https://openalex.org/W2964125708","https://openalex.org/W2964204553","https://openalex.org/W2971082124","https://openalex.org/W2983315964","https://openalex.org/W2986057551","https://openalex.org/W3002210617","https://openalex.org/W3104725225","https://openalex.org/W4230339119","https://openalex.org/W4288618203","https://openalex.org/W4375802365","https://openalex.org/W6726381175","https://openalex.org/W6749271710","https://openalex.org/W6759001153","https://openalex.org/W6762734363"],"related_works":["https://openalex.org/W1911817635","https://openalex.org/W3046997388","https://openalex.org/W1631562952","https://openalex.org/W2186922242","https://openalex.org/W1487093003","https://openalex.org/W3127099778","https://openalex.org/W1994564088","https://openalex.org/W2372643369","https://openalex.org/W2968742862","https://openalex.org/W2537559174"],"abstract_inverted_index":{"Convolutional":[0],"neural":[1],"network":[2],"(CNN)-based":[3],"denoisers":[4,33,99,206],"are":[5,61,70,87,101,134],"recently":[6,163],"shown":[7],"to":[8,50,194],"overwhelm":[9],"the":[10,14,22,39,51,57,79,83,112,128,168,195,208],"denoising":[11,41,75,120],"performances":[12,42],"of":[13,111,167,197],"conventional":[15,205],"prior-":[16],"or":[17],"optimization-based":[18],"methods,":[19],"particularly":[20],"for":[21,43,65,90,117,136,172,207],"additive":[23],"white":[24],"Gaussian":[25],"noise":[26,80,129],"(AWGN)":[27],"case.":[28],"However,":[29],"those":[30],"typical":[31],"CNN-based":[32,203],"had":[34],"two":[35,152],"main":[36],"limitations:":[37],"(1)":[38],"superb":[40],"AWGN":[44],"do":[45],"not":[46],"necessarily":[47],"carry":[48],"over":[49],"signal-dependent":[52],"real-noise":[53,74,119,190],"cases":[54],"and":[55,130,204],"(2)":[56],"clean":[58,84],"source":[59,85],"images":[60,86,133,192],"required":[62],"as":[63],"targets":[64],"supervised":[66,91],"training.":[67,92,137],"Both":[68],"limitations":[69,153],"critical":[71],"in":[72,122],"practical":[73],"setting,":[76,210],"since":[77],"neither":[78],"characteristics":[81],"nor":[82],"readily":[88],"available":[89,135],"While":[93],"some":[94],"recent":[95],"work":[96],"proposed":[97],"CNN":[98],"that":[100],"trained":[102,186],"solely":[103,187],"with":[104,161,188],"noisy":[105,132,214],"images,":[106],"they":[107],"still":[108],"fall":[109],"short":[110],"classical":[113],"baselines,":[114],"e.g.,":[115],"BM3D,":[116],"blind":[118],"settings,":[121],"which":[123,149],"nothing":[124],"is":[125],"known":[126],"about":[127],"only":[131],"In":[138],"this":[139],"paper,":[140],"we":[141,180],"propose":[142],"Blind":[143],"Pixelwise":[144],"Affine":[145],"Image":[146],"DEnoiser":[147],"(BP-AIDE),":[148],"tackles":[150],"above":[151],"by":[154],"combining":[155],"variance":[156],"stabilizing":[157],"transformation":[158],"(VST)":[159],"technique":[160],"a":[162,173,178],"developed":[164],"unbiased":[165],"estimate":[166],"mean-squared":[169],"error":[170],"(MSE)":[171],"pixelwise":[174],"affine":[175],"denoiser.":[176],"As":[177],"result,":[179],"show":[181],"BP-AIDE":[182],"can":[183],"be":[184],"blindly":[185],"single":[189],"corrupted":[191],"and,":[193],"best":[196],"our":[198],"knowledge,":[199],"first":[200],"outperforms":[201],"both":[202],"same":[209],"on":[211],"several":[212],"real-world":[213],"image":[215],"benchmark":[216],"datasets.":[217]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}