{"id":"https://openalex.org/W2539014756","doi":"https://doi.org/10.1109/aipr.2011.6176339","title":"Image denoising with a multi-phase kernel principal component approach and an ensemble version","display_name":"Image denoising with a multi-phase kernel principal component approach and an ensemble version","publication_year":2011,"publication_date":"2011-10-01","ids":{"openalex":"https://openalex.org/W2539014756","doi":"https://doi.org/10.1109/aipr.2011.6176339","mag":"2539014756"},"language":"en","primary_location":{"id":"doi:10.1109/aipr.2011.6176339","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr.2011.6176339","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","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/A5101679468","display_name":"Anshuman Sahu","orcid":"https://orcid.org/0009-0004-9461-9142"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anshuman Sahu","raw_affiliation_strings":["Department of Industrial Engineering, SCIDSE, Arizona State University, Tempe, AZ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, SCIDSE, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002544391","display_name":"George C. Runger","orcid":"https://orcid.org/0000-0001-9460-6983"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"George Runger","raw_affiliation_strings":["Department of Industrial Engineering, SCIDSE, Arizona State University, Tempe, AZ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, SCIDSE, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050269446","display_name":"Daniel W. Apley","orcid":"https://orcid.org/0000-0002-8545-4612"},"institutions":[{"id":"https://openalex.org/I4210155590","display_name":"Management Sciences (United States)","ror":"https://ror.org/05shz5j84","country_code":"US","type":"company","lineage":["https://openalex.org/I4210155590"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel Apley","raw_affiliation_strings":["Department of Industrial Engineering and Management Sciences, Evanston, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering and Management Sciences, Evanston, IL, USA","institution_ids":["https://openalex.org/I4210155590"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0468,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.82058314,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9994999766349792,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9966999888420105,"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/kernel-principal-component-analysis","display_name":"Kernel principal component analysis","score":0.6966236233711243},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6229941248893738},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5882970094680786},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5438244342803955},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5217629075050354},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5187366604804993},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.49738672375679016},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.49557197093963623},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4682316184043884},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.37130114436149597},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.30912065505981445},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.22650784254074097}],"concepts":[{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.6966236233711243},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6229941248893738},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5882970094680786},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5438244342803955},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5217629075050354},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5187366604804993},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.49738672375679016},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.49557197093963623},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4682316184043884},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37130114436149597},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.30912065505981445},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.22650784254074097},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aipr.2011.6176339","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr.2011.6176339","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1526146785","https://openalex.org/W1755117326","https://openalex.org/W1790954942","https://openalex.org/W1972832829","https://openalex.org/W1998269045","https://openalex.org/W2105732805","https://openalex.org/W2132773116","https://openalex.org/W2133396101","https://openalex.org/W2134296096","https://openalex.org/W2140095548","https://openalex.org/W2153557925","https://openalex.org/W2611015177","https://openalex.org/W6631556622","https://openalex.org/W6637962894","https://openalex.org/W6676014314","https://openalex.org/W6679352219","https://openalex.org/W7039177067"],"related_works":["https://openalex.org/W2379488555","https://openalex.org/W2753886092","https://openalex.org/W2152632846","https://openalex.org/W1992961908","https://openalex.org/W2014683590","https://openalex.org/W2944973397","https://openalex.org/W3138125914","https://openalex.org/W2398887903","https://openalex.org/W2127229869","https://openalex.org/W2004465977"],"abstract_inverted_index":{"Image":[0],"denoising":[1,29],"is":[2,40,88,106,125],"an":[3,100,126],"important":[4,101],"technique":[5],"of":[6,49,90,129,140,144,153,199,208],"practical":[7],"significance":[8],"which":[9,33],"serves":[10],"as":[11],"a":[12,64,114,118],"preliminary":[13],"step":[14],"for":[15,28,184],"other":[16],"analyses":[17],"like":[18],"image":[19,22,97],"feature":[20],"extraction,":[21],"classification":[23],"etc.":[24],"Two":[25],"novel":[26],"methods":[27,147,157,169],"images":[30],"are":[31,158,170],"proposed":[32,146],"deal":[34],"with":[35],"the":[36,52,57,68,79,84,91,108,130,141,145,185,209],"case":[37],"when":[38],"there":[39],"no":[41],"noise-free":[42],"training":[43],"data.":[44],"The":[45,122],"basic":[46,131],"method":[47,124,132],"consists":[48],"several":[50],"phases:":[51],"first":[53,85],"phase":[54,70],"involves":[55,71],"preprocessing":[56],"given":[58],"noisy":[59,137],"data":[60],"matrix":[61,81],"to":[62,96,136],"obtain":[63],"good":[65],"approximation":[66,80],"matrix;":[67],"second":[69,123],"implementing":[72],"kernel":[73],"principal":[74],"component":[75],"analysis":[76],"(KPCA)":[77],"on":[78,160,172],"obtained":[82],"from":[83],"phase.":[86],"KPCA":[87,105],"one":[89],"useful":[92],"non-linear":[93],"techniques":[94],"applied":[95],"denoising.":[98],"However,":[99],"problem":[102],"faced":[103],"in":[104,197],"estimating":[107],"denoised":[109],"pre-image.":[110],"Consequently,":[111],"we":[112],"generate":[113],"pre-image":[115],"by":[116],"solving":[117],"regularized":[119],"regression":[120],"problem.":[121],"ensemble":[127],"version":[128],"that":[133],"provides":[134],"robustness":[135],"instances.":[138],"Some":[139],"attractive":[142],"properties":[143],"include":[148],"numerical":[149],"stability":[150],"and":[151,163,179,181,189,204],"ease":[152],"implementation.":[154],"Also":[155],"our":[156],"based":[159],"linear":[161],"algebra":[162],"avoid":[164],"any":[165],"nonlinear":[166],"optimization.":[167],"Our":[168],"demonstrated":[171],"high-noise":[173],"cases":[174],"(for":[175],"both":[176,196],"Gaussian":[177],"noise":[178],"\u201csalt":[180],"pepper\u201d":[182],"noise)":[183],"USPS":[186],"digits":[187],"dataset,":[188],"they":[190],"perform":[191],"better":[192,205],"than":[193],"existing":[194],"alternatives":[195],"terms":[198],"low":[200],"mean":[201],"square":[202],"error":[203],"visual":[206],"quality":[207],"reconstructed":[210],"pre-images.":[211]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
