{"id":"https://openalex.org/W1493360757","doi":"https://doi.org/10.1109/iscas.2015.7169105","title":"Image denoising utilizing the scale-dependency in the contourlet domain","display_name":"Image denoising utilizing the scale-dependency in the contourlet domain","publication_year":2015,"publication_date":"2015-05-01","ids":{"openalex":"https://openalex.org/W1493360757","doi":"https://doi.org/10.1109/iscas.2015.7169105","mag":"1493360757"},"language":"en","primary_location":{"id":"doi:10.1109/iscas.2015.7169105","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas.2015.7169105","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Symposium on Circuits and Systems (ISCAS)","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/A5072288399","display_name":"Hamidreza Sadreazami","orcid":"https://orcid.org/0000-0002-2624-0681"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"H. Sadreazami","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada","Department of Electrical and Computer Engineering, Concordia University Montreal, Canada H3G 1M8"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University Montreal, Canada H3G 1M8","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068820891","display_name":"M. Omair Ahmad","orcid":"https://orcid.org/0000-0002-2924-6659"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"M. Omair Ahmad","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada","Department of Electrical and Computer Engineering, Concordia University Montreal, Canada H3G 1M8"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University Montreal, Canada H3G 1M8","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013967994","display_name":"M.N.S. Swamy","orcid":"https://orcid.org/0000-0002-3989-5476"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"M. N. S. Swamy","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada","Department of Electrical and Computer Engineering, Concordia University Montreal, Canada H3G 1M8"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University Montreal, Canada H3G 1M8","institution_ids":["https://openalex.org/I60158472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I60158472"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"124","issue":null,"first_page":"2149","last_page":"2152"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9997000098228455,"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":0.9781000018119812,"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/contourlet","display_name":"Contourlet","score":0.9541282653808594},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6472067832946777},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6288395524024963},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6093707084655762},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.6023391485214233},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.598335325717926},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5969251990318298},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.5561173558235168},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.49993348121643066},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.4444112479686737},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.4403541088104248},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.42800039052963257},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.41606011986732483},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4147428274154663},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.406074196100235},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3875438868999481},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.37260085344314575},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.306544691324234},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.26148802042007446},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.11785608530044556}],"concepts":[{"id":"https://openalex.org/C20479862","wikidata":"https://www.wikidata.org/wiki/Q5165589","display_name":"Contourlet","level":4,"score":0.9541282653808594},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6472067832946777},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6288395524024963},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6093707084655762},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.6023391485214233},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.598335325717926},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5969251990318298},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.5561173558235168},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.49993348121643066},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.4444112479686737},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.4403541088104248},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.42800039052963257},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.41606011986732483},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4147428274154663},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.406074196100235},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3875438868999481},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.37260085344314575},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.306544691324234},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.26148802042007446},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.11785608530044556},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscas.2015.7169105","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas.2015.7169105","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Symposium on Circuits and Systems (ISCAS)","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":19,"referenced_works":["https://openalex.org/W1522590968","https://openalex.org/W1965445419","https://openalex.org/W1970961068","https://openalex.org/W1972697625","https://openalex.org/W2039117831","https://openalex.org/W2048764473","https://openalex.org/W2061829113","https://openalex.org/W2084488391","https://openalex.org/W2090350213","https://openalex.org/W2112545418","https://openalex.org/W2113945798","https://openalex.org/W2125527601","https://openalex.org/W2128989367","https://openalex.org/W2134929491","https://openalex.org/W2135209903","https://openalex.org/W2158940042","https://openalex.org/W2163612361","https://openalex.org/W4214806317","https://openalex.org/W6631284433"],"related_works":["https://openalex.org/W2377340795","https://openalex.org/W1974392290","https://openalex.org/W1911817635","https://openalex.org/W2074565401","https://openalex.org/W2810224748","https://openalex.org/W2011915004","https://openalex.org/W2353656796","https://openalex.org/W1967187784","https://openalex.org/W3103566292","https://openalex.org/W1607924090"],"abstract_inverted_index":{"A":[0],"new":[1],"contourlet-based":[2],"method":[3,18,87,102],"is":[4,40,53],"introduced":[5],"for":[6],"reducing":[7],"noise":[8],"in":[9,56,109],"images":[10,91],"corrupted":[11],"by":[12,68,93],"additive":[13],"white":[14],"Gaussian":[15],"noise.":[16,95],"This":[17,51],"takes":[19],"into":[20],"account":[21],"the":[22,26,44,48,65,81,84,100,106,113],"statistical":[23],"dependencies":[24,46],"among":[25],"contourlet":[27,49],"coefficients":[28,67],"of":[29,34,47,83,105,111],"different":[30],"scales.":[31],"In":[32],"view":[33],"this,":[35],"a":[36,57,60],"non-Gaussian":[37],"multivariate":[38],"distribution":[39],"proposed":[41,85,101],"to":[42,63,79],"capture":[43],"across-scale":[45],"coefficients.":[50],"model":[52],"then":[54],"exploited":[55],"Bayesian":[58],"maximum":[59],"posteriori":[61],"estimator":[62],"restore":[64],"clean":[66],"deriving":[69],"an":[70],"efficient":[71],"closed-form":[72],"shrinkage":[73],"function.":[74],"Experimental":[75],"results":[76,97],"are":[77],"performed":[78],"evaluate":[80],"performance":[82],"denoising":[86],"using":[88],"typical":[89],"noise-free":[90],"contaminated":[92],"simulated":[94],"The":[96],"show":[98],"that":[99],"outperforms":[103],"some":[104],"state-of-the-art":[107],"methods":[108],"terms":[110],"both":[112],"subjective":[114],"and":[115],"objective":[116],"criteria.":[117]},"counts_by_year":[{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}