{"id":"https://openalex.org/W2897557956","doi":"https://doi.org/10.1109/icme.2018.8486558","title":"Color Image Noise Covariance Estimation with Cross-Channel Image Noise Modeling","display_name":"Color Image Noise Covariance Estimation with Cross-Channel Image Noise Modeling","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2897557956","doi":"https://doi.org/10.1109/icme.2018.8486558","mag":"2897557956"},"language":"en","primary_location":{"id":"doi:10.1109/icme.2018.8486558","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme.2018.8486558","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Multimedia and Expo (ICME)","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/A5100771361","display_name":"Li Dong","orcid":"https://orcid.org/0000-0003-2002-8249"},"institutions":[{"id":"https://openalex.org/I204512498","display_name":"University of Macau","ror":"https://ror.org/01r4q9n85","country_code":"MO","type":"education","lineage":["https://openalex.org/I204512498"]}],"countries":["MO"],"is_corresponding":true,"raw_author_name":"Li Dong","raw_affiliation_strings":["Department of Computer and Information Science, University of Macau, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer and Information Science, University of Macau, China","institution_ids":["https://openalex.org/I204512498"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037979193","display_name":"Jiantao Zhou","orcid":"https://orcid.org/0000-0002-6015-2618"},"institutions":[{"id":"https://openalex.org/I204512498","display_name":"University of Macau","ror":"https://ror.org/01r4q9n85","country_code":"MO","type":"education","lineage":["https://openalex.org/I204512498"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Jiantao Zhou","raw_affiliation_strings":["Department of Computer and Information Science, University of Macau, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer and Information Science, University of Macau, China","institution_ids":["https://openalex.org/I204512498"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023762528","display_name":"Tao Dai","orcid":"https://orcid.org/0000-0003-0594-6404"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]},{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Dai","raw_affiliation_strings":["Graduate School at Shenzhen., Tsinghua University, Shenzhen, Guangdong, China"],"affiliations":[{"raw_affiliation_string":"Graduate School at Shenzhen., Tsinghua University, Shenzhen, Guangdong, China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100771361"],"corresponding_institution_ids":["https://openalex.org/I204512498"],"apc_list":null,"apc_paid":null,"fwci":0.6267,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.74547978,"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":"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":0.9998999834060669,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9944000244140625,"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"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9828000068664551,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6217250823974609},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5910318493843079},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.5833308100700378},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5793339014053345},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5739083290100098},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.4984769821166992},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.46141302585601807},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4598906636238098},{"id":"https://openalex.org/keywords/image-noise","display_name":"Image noise","score":0.4586155414581299},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4475935101509094},{"id":"https://openalex.org/keywords/estimation-of-covariance-matrices","display_name":"Estimation of covariance matrices","score":0.4412805438041687},{"id":"https://openalex.org/keywords/value-noise","display_name":"Value noise","score":0.4372367560863495},{"id":"https://openalex.org/keywords/covariance-intersection","display_name":"Covariance intersection","score":0.42441967129707336},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.41788914799690247},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.36126989126205444},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.332651823759079},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.25383031368255615},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1954725980758667},{"id":"https://openalex.org/keywords/noise-floor","display_name":"Noise floor","score":0.07757073640823364}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6217250823974609},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5910318493843079},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.5833308100700378},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5793339014053345},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5739083290100098},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.4984769821166992},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.46141302585601807},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4598906636238098},{"id":"https://openalex.org/C35772409","wikidata":"https://www.wikidata.org/wiki/Q1323086","display_name":"Image noise","level":3,"score":0.4586155414581299},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4475935101509094},{"id":"https://openalex.org/C180877172","wikidata":"https://www.wikidata.org/wiki/Q5401390","display_name":"Estimation of covariance matrices","level":3,"score":0.4412805438041687},{"id":"https://openalex.org/C182163834","wikidata":"https://www.wikidata.org/wiki/Q2926529","display_name":"Value noise","level":5,"score":0.4372367560863495},{"id":"https://openalex.org/C83042196","wikidata":"https://www.wikidata.org/wiki/Q5178898","display_name":"Covariance intersection","level":4,"score":0.42441967129707336},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.41788914799690247},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36126989126205444},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.332651823759079},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.25383031368255615},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1954725980758667},{"id":"https://openalex.org/C187612029","wikidata":"https://www.wikidata.org/wiki/Q17083130","display_name":"Noise floor","level":4,"score":0.07757073640823364}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme.2018.8486558","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme.2018.8486558","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1592510033","https://openalex.org/W2027898483","https://openalex.org/W2047710600","https://openalex.org/W2062515778","https://openalex.org/W2171125155","https://openalex.org/W2219841864","https://openalex.org/W2469031810","https://openalex.org/W2566815267","https://openalex.org/W2963315679","https://openalex.org/W6662481968"],"related_works":["https://openalex.org/W2005333371","https://openalex.org/W2013771251","https://openalex.org/W1969252538","https://openalex.org/W2955414824","https://openalex.org/W4391267839","https://openalex.org/W4297491189","https://openalex.org/W1964290457","https://openalex.org/W2016976236","https://openalex.org/W2124371593","https://openalex.org/W2004836404"],"abstract_inverted_index":{"Noise":[0],"estimation":[1,17,120,129],"is":[2,88,148],"crucial":[3],"in":[4,55,58],"many":[5],"image":[6,152],"processing":[7],"tasks":[8],"such":[9],"as":[10],"denoising.":[11,153],"Most":[12],"of":[13],"the":[14,38,53,63,75,80,98,104,110,123,127,142],"existing":[15],"noise":[16,54,76,112,118,143],"methods":[18,29],"are":[19,130],"specially":[20],"developed":[21],"for":[22,73],"grayscale":[23],"images.":[24],"For":[25],"color":[26,33,56,66,151],"images,":[27,57],"these":[28],"simply":[30],"handle":[31],"each":[32],"channel":[34],"independently,":[35],"without":[36],"considering":[37],"correlation":[39],"across":[40],"channels.":[41,67],"In":[42],"this":[43],"work,":[44],"we":[45,60,114],"propose":[46],"a":[47,70,84],"multivariate":[48],"Gaussian":[49],"approach":[50],"to":[51,91],"model":[52],"which":[59],"explicitly":[61],"consider":[62],"inter-dependence":[64],"among":[65],"We":[68],"design":[69],"practical":[71,146],"method":[72,138],"estimating":[74],"covariance":[77,119,128],"matrix":[78],"within":[79],"proposed":[81],"model.":[82],"Specifically,":[83],"patch":[85,105,124],"selection":[86,106,125],"scheme":[87],"first":[89],"introduced":[90],"select":[92],"weakly":[93],"textured":[94],"patches":[95],"through":[96],"thresholding":[97],"texture":[99],"strength":[100],"indicators.":[101],"Noticing":[102],"that":[103,136],"actually":[107],"depends":[108],"on":[109],"unknown":[111],"covariance,":[113],"present":[115],"an":[116],"iterative":[117],"algorithm,":[121],"where":[122],"and":[126],"conducted":[131],"alternately.":[132],"Experimental":[133],"results":[134],"show":[135],"our":[137],"can":[139],"effectively":[140],"estimate":[141],"covariance.":[144],"The":[145],"usage":[147],"demonstrated":[149],"with":[150]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
