{"id":"https://openalex.org/W1978350129","doi":"https://doi.org/10.1109/fcv.2013.6485451","title":"High density impulse noise removal based on linear mean-median filter","display_name":"High density impulse noise removal based on linear mean-median filter","publication_year":2013,"publication_date":"2013-01-01","ids":{"openalex":"https://openalex.org/W1978350129","doi":"https://doi.org/10.1109/fcv.2013.6485451","mag":"1978350129"},"language":"en","primary_location":{"id":"doi:10.1109/fcv.2013.6485451","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fcv.2013.6485451","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision","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/A5015612169","display_name":"Fitri Utaminingrum","orcid":"https://orcid.org/0000-0002-0281-9429"},"institutions":[{"id":"https://openalex.org/I96036126","display_name":"Kumamoto University","ror":"https://ror.org/02cgss904","country_code":"JP","type":"education","lineage":["https://openalex.org/I96036126"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Fitri Utaminingrum","raw_affiliation_strings":["Computer Science and Electrical Engineering, Graduate School of Science and Technology, Kumamoto University, Kumamoto, Japan","Grad. Sch. of Sci. & Technol., Kumamoto Univ., Kumamoto, Japan#TAB#"],"affiliations":[{"raw_affiliation_string":"Computer Science and Electrical Engineering, Graduate School of Science and Technology, Kumamoto University, Kumamoto, Japan","institution_ids":["https://openalex.org/I96036126"]},{"raw_affiliation_string":"Grad. Sch. of Sci. & Technol., Kumamoto Univ., Kumamoto, Japan#TAB#","institution_ids":["https://openalex.org/I96036126"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111555953","display_name":"K. UCHIMURA","orcid":null},"institutions":[{"id":"https://openalex.org/I96036126","display_name":"Kumamoto University","ror":"https://ror.org/02cgss904","country_code":"JP","type":"education","lineage":["https://openalex.org/I96036126"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Keiichia Uchimura","raw_affiliation_strings":["Computer Science and Electrical Engineering, Graduate School of Science and Technology, Kumamoto University, Kumamoto, Japan","Grad. Sch. of Sci. & Technol., Kumamoto Univ., Kumamoto, Japan#TAB#"],"affiliations":[{"raw_affiliation_string":"Computer Science and Electrical Engineering, Graduate School of Science and Technology, Kumamoto University, Kumamoto, Japan","institution_ids":["https://openalex.org/I96036126"]},{"raw_affiliation_string":"Grad. Sch. of Sci. & Technol., Kumamoto Univ., Kumamoto, Japan#TAB#","institution_ids":["https://openalex.org/I96036126"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016988662","display_name":"Gou Koutaki","orcid":"https://orcid.org/0000-0002-3414-1085"},"institutions":[{"id":"https://openalex.org/I96036126","display_name":"Kumamoto University","ror":"https://ror.org/02cgss904","country_code":"JP","type":"education","lineage":["https://openalex.org/I96036126"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Gou Koutaki","raw_affiliation_strings":["Computer Science and Electrical Engineering, Graduate School of Science and Technology, Kumamoto University, Kumamoto, Japan","Grad. Sch. of Sci. & Technol., Kumamoto Univ., Kumamoto, Japan#TAB#"],"affiliations":[{"raw_affiliation_string":"Computer Science and Electrical Engineering, Graduate School of Science and Technology, Kumamoto University, Kumamoto, Japan","institution_ids":["https://openalex.org/I96036126"]},{"raw_affiliation_string":"Grad. Sch. of Sci. & Technol., Kumamoto Univ., Kumamoto, Japan#TAB#","institution_ids":["https://openalex.org/I96036126"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5015612169"],"corresponding_institution_ids":["https://openalex.org/I96036126"],"apc_list":null,"apc_paid":null,"fwci":1.3805,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.83069609,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"11","last_page":"17"},"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/T11019","display_name":"Image Enhancement Techniques","score":0.9933000206947327,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9925000071525574,"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/median-filter","display_name":"Median filter","score":0.7464771866798401},{"id":"https://openalex.org/keywords/impulse-noise","display_name":"Impulse noise","score":0.705353319644928},{"id":"https://openalex.org/keywords/salt-and-pepper-noise","display_name":"Salt-and-pepper noise","score":0.6576812267303467},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5383554100990295},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5124498009681702},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45743563771247864},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.41335272789001465},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3454556167125702},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.34096717834472656},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3345809578895569},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.13841840624809265},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.09639957547187805}],"concepts":[{"id":"https://openalex.org/C55352655","wikidata":"https://www.wikidata.org/wiki/Q304247","display_name":"Median filter","level":4,"score":0.7464771866798401},{"id":"https://openalex.org/C127372701","wikidata":"https://www.wikidata.org/wiki/Q16979398","display_name":"Impulse noise","level":3,"score":0.705353319644928},{"id":"https://openalex.org/C113660513","wikidata":"https://www.wikidata.org/wiki/Q849379","display_name":"Salt-and-pepper noise","level":5,"score":0.6576812267303467},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5383554100990295},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5124498009681702},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45743563771247864},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.41335272789001465},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3454556167125702},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.34096717834472656},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3345809578895569},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.13841840624809265},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.09639957547187805}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fcv.2013.6485451","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fcv.2013.6485451","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320335079","display_name":"Direktorat Jenderal Pendidikan Tinggi","ror":"https://ror.org/03sxt1c89"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1601760457","https://openalex.org/W1979072167","https://openalex.org/W1987739142","https://openalex.org/W2013396801","https://openalex.org/W2027495287","https://openalex.org/W2076537250","https://openalex.org/W2093722317","https://openalex.org/W2098615715","https://openalex.org/W2105218800","https://openalex.org/W2123992117","https://openalex.org/W2476868371","https://openalex.org/W2499334499","https://openalex.org/W6724004949"],"related_works":["https://openalex.org/W2953655071","https://openalex.org/W2121479027","https://openalex.org/W2082346846","https://openalex.org/W1978900443","https://openalex.org/W2415066633","https://openalex.org/W1787910866","https://openalex.org/W2545973440","https://openalex.org/W2518478289","https://openalex.org/W2167707673","https://openalex.org/W2041083590"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"Linear":[3],"Mean-Median":[4],"(LMM)":[5],"filter":[6,14,39,109,139],"that":[7,50,90,117],"used":[8],"to":[9,57,99,122,141,180],"reduce":[10],"impulse":[11,113],"noise.":[12],"LMM":[13,94,138],"is":[15,26,64,83,131,203],"a":[16,101,105,120],"combination":[17],"between":[18,31],"Mean":[19,36],"and":[20,33,37,164],"Median":[21,38],"filter.":[22],"Wherein,":[23],"linear":[24],"value":[25,63,69,189],"acquired":[27],"from":[28,54,66],"the":[29,47,55,58,67,76,84,88,112,129,135,161,165,191,196,206],"linearity":[30],"mean":[32,62,149],"median":[34,77,81],"value.":[35,60],"are":[40],"only":[41],"applied":[42],"for":[43,103],"free-noise":[44,72],"pixel":[45,78,82,89,102],"on":[46],"3\u00d73":[48],"windows":[49],"has":[51,91],"been":[52,92],"sorted":[53],"smallest":[56],"largest":[59],"The":[61,151],"obtained":[65],"average":[68],"of":[70,87,137,160,200],"all":[71],"pixels":[73,98],"without":[74],"including":[75],"position.":[79],"Meanwhile,":[80],"middle":[85],"position":[86],"sorted.":[93],"uses":[95],"nine":[96],"sample":[97],"determine":[100],"replacement":[104],"corrupted":[106],"pixel.":[107],"Our":[108,183],"also":[110],"provides":[111],"noise":[114,126,130],"prediction":[115],"systems":[116],"serve":[118],"as":[119],"facilitator":[121],"give":[123],"information":[124],"about":[125],"content.":[127],"If":[128],"greater":[132],"than":[133,190,205],"30%,":[134],"performance":[136],"needs":[140],"be":[142,174],"improved":[143],"by":[144,176],"an":[145],"adaptive":[146],"rank":[147],"order":[148],"filters.":[150],"filtering":[152],"results":[153,157],"have":[154,186],"shown":[155],"satisfactory":[156],"in":[158],"terms":[159],"quality":[162,172],"result":[163],"computation":[166,198],"time":[167,199],"process.":[168],"A":[169],"good":[170],"image":[171],"can":[173],"evidenced":[175],"PSNR":[177,188],"(Peak":[178],"Signal":[179],"Noise":[181],"Ratio).":[182],"methods":[184],"always":[185],"higher":[187],"comparison":[192,207],"methods.":[193],"In":[194],"addition,":[195],"speed":[197],"our":[201],"method":[202],"faster":[204],"method.":[208]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":2}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}
