{"id":"https://openalex.org/W2035425462","doi":"https://doi.org/10.1109/lsp.2012.2217329","title":"Non-Local Euclidean Medians","display_name":"Non-Local Euclidean Medians","publication_year":2012,"publication_date":"2012-09-05","ids":{"openalex":"https://openalex.org/W2035425462","doi":"https://doi.org/10.1109/lsp.2012.2217329","mag":"2035425462","pmid":"https://pubmed.ncbi.nlm.nih.gov/24817813"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2012.2217329","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2012.2217329","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":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1207.3056","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"K. N. Chaudhury","orcid":null},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"K. N. Chaudhury","raw_affiliation_strings":["Program in Applied and Computational Mathematics (PACM), Princeton University, Princeton, NJ 08544 USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Program in Applied and Computational Mathematics (PACM), Princeton University, Princeton, NJ 08544 USA","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"last","author":{"id":null,"display_name":"A. Singer","orcid":null},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"A. Singer","raw_affiliation_strings":["PACM and Department of Mathematics, Princeton University, Princeton, NJ 08544 USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"PACM and Department of Mathematics, Princeton University, Princeton, NJ 08544 USA","institution_ids":["https://openalex.org/I20089843"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I20089843"],"apc_list":null,"apc_paid":null,"fwci":6.2649,"has_fulltext":false,"cited_by_count":62,"citation_normalized_percentile":{"value":0.9705954,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"19","issue":"11","first_page":"745","last_page":"748"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.4251999855041504,"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":0.4251999855041504,"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":0.056299999356269836,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.051899999380111694,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6830999851226807},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.6632999777793884},{"id":"https://openalex.org/keywords/euclidean-geometry","display_name":"Euclidean geometry","score":0.5971999764442444},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5703999996185303},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5295000076293945},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.48570001125335693},{"id":"https://openalex.org/keywords/median","display_name":"Median","score":0.48330000042915344},{"id":"https://openalex.org/keywords/euclidean-distance-matrix","display_name":"Euclidean distance matrix","score":0.4575999975204468},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.4357999861240387}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6830999851226807},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.6632999777793884},{"id":"https://openalex.org/C129782007","wikidata":"https://www.wikidata.org/wiki/Q162886","display_name":"Euclidean geometry","level":2,"score":0.5971999764442444},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5703999996185303},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.554099977016449},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5297999978065491},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5295000076293945},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.48570001125335693},{"id":"https://openalex.org/C156460124","wikidata":"https://www.wikidata.org/wiki/Q235001","display_name":"Median","level":2,"score":0.48330000042915344},{"id":"https://openalex.org/C171558263","wikidata":"https://www.wikidata.org/wiki/Q5406122","display_name":"Euclidean distance matrix","level":3,"score":0.4575999975204468},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.4357999861240387},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4092000126838684},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3815000057220459},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35830000042915344},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.34279999136924744},{"id":"https://openalex.org/C148764684","wikidata":"https://www.wikidata.org/wiki/Q621751","display_name":"Approximation algorithm","level":2,"score":0.3240000009536743},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.3138999938964844},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.31360000371932983},{"id":"https://openalex.org/C311688","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Time complexity","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2865000069141388},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.27250000834465027},{"id":"https://openalex.org/C29123130","wikidata":"https://www.wikidata.org/wiki/Q874709","display_name":"Computational geometry","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C19939924","wikidata":"https://www.wikidata.org/wiki/Q852195","display_name":"Euclidean group","level":4,"score":0.2597000002861023},{"id":"https://openalex.org/C23719512","wikidata":"https://www.wikidata.org/wiki/Q867345","display_name":"Euclidean domain","level":4,"score":0.257999986410141},{"id":"https://openalex.org/C106516650","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm design","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C97542219","wikidata":"https://www.wikidata.org/wiki/Q497863","display_name":"SIMPLE algorithm","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C2984485829","wikidata":"https://www.wikidata.org/wiki/Q100766421","display_name":"Noise level","level":3,"score":0.2551000118255615},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2549000084400177}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/lsp.2012.2217329","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2012.2217329","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"},{"id":"pmid:24817813","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/24817813","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE signal processing letters","raw_type":null},{"id":"pmh:oai:arXiv.org:1207.3056","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1207.3056","pdf_url":"https://arxiv.org/pdf/1207.3056","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.748.758","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.748.758","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://arxiv.org/pdf/1207.3056.pdf","raw_type":"text"},{"id":"pmh:oai:europepmc.org:2991457","is_oa":false,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/4013021","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1207.3056","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1207.3056","pdf_url":"https://arxiv.org/pdf/1207.3056","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1439776790","display_name":null,"funder_award_id":"R01 GM090200","funder_id":"https://openalex.org/F4320337354","funder_display_name":"National Institute of General Medical Sciences"},{"id":"https://openalex.org/G5140845023","display_name":"Improved algorithms for three-dimensional determination of macromolecules by cryo-electron microscopy and NMR spectroscopy","funder_award_id":"135867","funder_id":"https://openalex.org/F4320320924","funder_display_name":"Schweizerischer Nationalfonds zur F\u00f6rderung der Wissenschaftlichen Forschung"}],"funders":[{"id":"https://openalex.org/F4320320924","display_name":"Schweizerischer Nationalfonds zur F\u00f6rderung der Wissenschaftlichen Forschung","ror":"https://ror.org/00yjd3n13"},{"id":"https://openalex.org/F4320337354","display_name":"National Institute of General Medical Sciences","ror":"https://ror.org/04q48ey07"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2022204379","https://openalex.org/W2083609718","https://openalex.org/W2083987637","https://openalex.org/W2119883478","https://openalex.org/W2133665775","https://openalex.org/W2136396015","https://openalex.org/W2161037052","https://openalex.org/W2162053094","https://openalex.org/W2168745297","https://openalex.org/W2539075699","https://openalex.org/W4205806204"],"related_works":[],"abstract_inverted_index":{"In":[0,58],"this":[1,30],"letter,":[2],"we":[3,60],"note":[4],"that":[5,47,66,101],"the":[6,22,25,34,40,45,48,56,75,111],"denoising":[7,32],"performance":[8],"of":[9,42,77,102],"Non-Local":[10,35],"Means":[11],"(NLM)":[12],"can":[13,85],"be":[14,86],"improved":[15],"at":[16,80],"large":[17,81],"noise":[18,82],"levels":[19],"by":[20,24],"replacing":[21],"mean":[23],"Euclidean":[26,36],"median.":[27],"We":[28,104],"call":[29],"new":[31],"algorithm":[33,113],"Medians":[37],"(NLEM).":[38],"At":[39],"heart":[41],"NLEM":[43,69,84],"is":[44,50,98],"observation":[46],"median":[49],"more":[51],"robust":[52],"to":[53,100,109,115],"outliers":[54],"than":[55,72],"mean.":[57],"particular,":[59],"provide":[61,105],"a":[62],"simple":[63],"geometric":[64],"insight":[65],"explains":[67],"why":[68],"performs":[70],"better":[71],"NLM":[73],"in":[74],"vicinity":[76],"edges,":[78],"particularly":[79],"levels.":[83],"efficiently":[87],"implemented":[88],"using":[89],"iteratively":[90],"reweighted":[91],"least":[92],"squares,":[93],"and":[94,114],"its":[95],"computational":[96],"complexity":[97],"comparable":[99],"NLM.":[103,119],"some":[106],"preliminary":[107],"results":[108],"study":[110],"proposed":[112],"compare":[116],"it":[117],"with":[118]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":9},{"year":2017,"cited_by_count":7},{"year":2016,"cited_by_count":7},{"year":2015,"cited_by_count":10},{"year":2014,"cited_by_count":8},{"year":2013,"cited_by_count":5}],"updated_date":"2026-07-02T09:51:11.867554","created_date":"2025-10-10T00:00:00"}
