{"id":"https://openalex.org/W1986989009","doi":"https://doi.org/10.1109/mmsp.2014.6958792","title":"Robust mixed noise removal with non-parametric Bayesian sparse outlier model","display_name":"Robust mixed noise removal with non-parametric Bayesian sparse outlier model","publication_year":2014,"publication_date":"2014-09-01","ids":{"openalex":"https://openalex.org/W1986989009","doi":"https://doi.org/10.1109/mmsp.2014.6958792","mag":"1986989009"},"language":"en","primary_location":{"id":"doi:10.1109/mmsp.2014.6958792","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp.2014.6958792","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE 16th International Workshop on Multimedia Signal Processing (MMSP)","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/A5082780960","display_name":"Peixian Zhuang","orcid":"https://orcid.org/0000-0002-7143-9569"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peixian Zhuang","raw_affiliation_strings":["College of information science and engineering, Xiamen University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of information science and engineering, Xiamen University, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100392009","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0002-5595-4552"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["College of information science and engineering, Xiamen University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of information science and engineering, Xiamen University, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005221942","display_name":"Delu Zeng","orcid":"https://orcid.org/0000-0001-7322-1873"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Delu Zeng","raw_affiliation_strings":["College of information science and engineering, Xiamen University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of information science and engineering, Xiamen University, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052820597","display_name":"Xinghao Ding","orcid":"https://orcid.org/0000-0003-2288-5287"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinghao Ding","raw_affiliation_strings":["College of information science and engineering, Xiamen University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of information science and engineering, Xiamen University, China","institution_ids":["https://openalex.org/I191208505"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I191208505"],"apc_list":null,"apc_paid":null,"fwci":0.1798,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.39807701,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"40","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9998000264167786,"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":0.9998000264167786,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11698","display_name":"Underwater Acoustics Research","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.7671757936477661},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6608741879463196},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.618122398853302},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.572654128074646},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.4839380383491516},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4769924283027649},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46483278274536133},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4329308867454529},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.42623668909072876},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3462206721305847},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2432156503200531},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.17765432596206665}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7671757936477661},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6608741879463196},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.618122398853302},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.572654128074646},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.4839380383491516},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4769924283027649},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46483278274536133},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4329308867454529},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.42623668909072876},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3462206721305847},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2432156503200531},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.17765432596206665},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mmsp.2014.6958792","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp.2014.6958792","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE 16th International Workshop on Multimedia Signal Processing (MMSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1969415786","https://openalex.org/W1973207753","https://openalex.org/W2002393993","https://openalex.org/W2014311222","https://openalex.org/W2020989074","https://openalex.org/W2045079989","https://openalex.org/W2049502219","https://openalex.org/W2056370875","https://openalex.org/W2060945009","https://openalex.org/W2086962710","https://openalex.org/W2104037165","https://openalex.org/W2113945798","https://openalex.org/W2117111086","https://openalex.org/W2122374500","https://openalex.org/W2126773133","https://openalex.org/W2141159272","https://openalex.org/W2160547390","https://openalex.org/W2400112622"],"related_works":["https://openalex.org/W3006513224","https://openalex.org/W2046456988","https://openalex.org/W2357409937","https://openalex.org/W2978674666","https://openalex.org/W2074430941","https://openalex.org/W2113096305","https://openalex.org/W2580722822","https://openalex.org/W2772305933","https://openalex.org/W1486338765","https://openalex.org/W4387454008"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3],"novel":[4],"non-parametric":[5,72],"Bayesian":[6,73],"framework":[7],"for":[8],"solving":[9],"mixed":[10,90,106],"noise":[11,22,44,78,91,107],"removal":[12],"problem.":[13],"In":[14],"order":[15],"to":[16,56,88],"removing":[17],"unstable":[18],"effects":[19],"of":[20,40,61,94],"outlier":[21,66],"such":[23],"as":[24],"salt-and-pepper":[25],"in":[26],"the":[27,32,49,58,70,77,81,89],"training":[28,82],"data,":[29,42],"we":[30],"decompose":[31],"observed":[33],"data":[34,63,83],"model":[35,51,74,95],"into":[36],"three":[37],"components":[38],"terms":[39],"ideal":[41],"Gaussian":[43],"and":[45,65,84,108],"sparse":[46,54],"outlier.":[47],"And":[48],"proposed":[50,71,101],"employs":[52],"spike-slab":[53],"prior":[55],"find":[57],"sparser":[59],"coefficients":[60],"desired":[62],"term":[64],"noise.":[67],"Note":[68],"that":[69],"can":[75],"infer":[76],"statistics":[79],"from":[80],"have":[85],"been":[86],"robust":[87],"without":[92],"tuning":[93],"parameters.":[96],"Experimental":[97],"results":[98],"demonstrate":[99],"our":[100],"algorithm":[102],"performs":[103],"well":[104],"with":[105],"achieves":[109],"better":[110],"performance":[111],"over":[112],"other":[113],"state-of-the-art":[114],"methods.":[115]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
