{"id":"https://openalex.org/W2057425218","doi":"https://doi.org/10.1109/mlsp.2012.6349731","title":"Towards dictionary learning from images with non Gaussian noise","display_name":"Towards dictionary learning from images with non Gaussian noise","publication_year":2012,"publication_date":"2012-09-01","ids":{"openalex":"https://openalex.org/W2057425218","doi":"https://doi.org/10.1109/mlsp.2012.6349731","mag":"2057425218"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp.2012.6349731","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2012.6349731","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","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/A5088402344","display_name":"Pierre Chainais","orcid":"https://orcid.org/0000-0003-4377-7584"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en sciences et technologies du num\u00e9rique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"government","lineage":["https://openalex.org/I1326498283"]},{"id":"https://openalex.org/I216852786","display_name":"\u00c9cole Centrale Paris","ror":"https://ror.org/00v0y5771","country_code":"FR","type":"education","lineage":["https://openalex.org/I216852786"]},{"id":"https://openalex.org/I4210143894","display_name":"Laboratoire d'Automatique, G\u00e9nie Informatique et Signal","ror":"https://ror.org/04wez4p43","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I2279609970","https://openalex.org/I4210131494","https://openalex.org/I4210137101","https://openalex.org/I4210139971","https://openalex.org/I4210143894","https://openalex.org/I7454413"]},{"id":"https://openalex.org/I7454413","display_name":"\u00c9cole Centrale de Lille","ror":"https://ror.org/01x441g73","country_code":"FR","type":"education","lineage":["https://openalex.org/I7454413"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Pierre Chainais","raw_affiliation_strings":["LAGIS CNRS UMR 8219-INRIA Lille-Nord Europe, Ecole Centrale de Lille, Villeneuve d'Ascq, France","Ecole Centrale Lille - LAGIS CNRS UMR 8219 - INRIA Lille-Nord Europe, SequeL, Avenue Paul Langevin - BP48 59651 Villeneuve d'Ascq Cedex France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LAGIS CNRS UMR 8219-INRIA Lille-Nord Europe, Ecole Centrale de Lille, Villeneuve d'Ascq, France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I1326498283","https://openalex.org/I4210143894","https://openalex.org/I7454413"]},{"raw_affiliation_string":"Ecole Centrale Lille - LAGIS CNRS UMR 8219 - INRIA Lille-Nord Europe, SequeL, Avenue Paul Langevin - BP48 59651 Villeneuve d'Ascq Cedex France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I216852786","https://openalex.org/I7454413"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5088402344"],"corresponding_institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I1326498283","https://openalex.org/I216852786","https://openalex.org/I4210143894","https://openalex.org/I7454413"],"apc_list":null,"apc_paid":null,"fwci":0.4512,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.60763279,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"99","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":0.9991999864578247,"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":0.9991999864578247,"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":0.9990000128746033,"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"}},{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.992900013923645,"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/gaussian-noise","display_name":"Gaussian noise","score":0.6573339700698853},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.6385289430618286},{"id":"https://openalex.org/keywords/vector-quantization","display_name":"Vector quantization","score":0.6263746023178101},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6208876371383667},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5975700616836548},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.597237229347229},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5429927110671997},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5313931703567505},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5012996196746826},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5012717247009277},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.4617685377597809},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4418468177318573},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.42322638630867004},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.4208185374736786},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4197794198989868},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2917974889278412},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.08577284216880798}],"concepts":[{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.6573339700698853},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6385289430618286},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.6263746023178101},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6208876371383667},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5975700616836548},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.597237229347229},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5429927110671997},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5313931703567505},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5012996196746826},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5012717247009277},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.4617685377597809},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4418468177318573},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.42322638630867004},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.4208185374736786},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4197794198989868},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2917974889278412},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.08577284216880798},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp.2012.6349731","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2012.6349731","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.7599999904632568,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1480376833","https://openalex.org/W1890834058","https://openalex.org/W1994163327","https://openalex.org/W2010491386","https://openalex.org/W2116857329","https://openalex.org/W2136396015","https://openalex.org/W2160547390","https://openalex.org/W2163168659","https://openalex.org/W2184852195","https://openalex.org/W2296399167","https://openalex.org/W2787894218","https://openalex.org/W6686418764"],"related_works":["https://openalex.org/W3209251257","https://openalex.org/W3108403339","https://openalex.org/W2162712524","https://openalex.org/W2986378528","https://openalex.org/W3178345791","https://openalex.org/W4287867034","https://openalex.org/W2175872006","https://openalex.org/W1550854977","https://openalex.org/W2949211747","https://openalex.org/W3005783148"],"abstract_inverted_index":{"We":[0,85,109,125,141],"address":[1],"the":[2,25,46,53,56,64,87,90,115,120],"problem":[3,16],"of":[4,55,58,78,89],"image":[5],"dictionary":[6,133],"learning":[7],"from":[8,148],"noisy":[9,81,107],"images":[10,149],"with":[11],"non":[12,70,105,151],"Gaussian":[13,44,71,106,152],"noise.":[14],"This":[15],"is":[17,36,49,83],"difficult.":[18],"As":[19],"a":[20,75,111,127,132],"first":[21],"step,":[22],"we":[23],"consider":[24],"extreme":[26],"sparse":[27],"code":[28],"given":[29],"by":[30,97,145],"vector":[31,128],"quantization,":[32],"i.e.":[33],"each":[34],"pixel":[35],"finally":[37],"associated":[38],"to":[39,103],"1":[40],"single":[41],"atom.":[42],"For":[43,69],"noise,":[45,153],"natural":[47],"solution":[48],"K-means":[50,118],"clustering":[51],"using":[52,119],"sum":[54],"squares":[57],"differences":[59],"between":[60,67,80],"gray":[61],"levels":[62],"as":[63],"dissimilarity":[65,79,123],"measure":[66,77],"patches.":[68,108],"noises":[72],"(Poisson,":[73],"Gamma,...),":[74],"new":[76],"patches":[82],"necessary.":[84],"study":[86],"use":[88],"generalized":[91],"likelihood":[92],"ratios":[93],"(GLR)":[94],"recently":[95],"introduced":[96],"Deledalle":[98],"et":[99],"al.":[100],"in":[101],"[1]":[102],"compare":[104],"propose":[110],"K-medoids":[112],"algorithm":[113],"generalizing":[114],"usual":[116],"Linde-Buzo-Gray":[117],"GLR":[121],"based":[122],"measure.":[124],"obtain":[126],"quantization":[129],"which":[130],"provides":[131],"that":[134],"can":[135],"be":[136],"very":[137],"large":[138],"and":[139,154],"redundant.":[140],"illustrate":[142],"our":[143],"approach":[144],"dictionaries":[146],"learnt":[147],"featuring":[150],"present":[155],"preliminary":[156],"denoising":[157],"results.":[158]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2014,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}