{"id":"https://openalex.org/W1550006058","doi":"https://doi.org/10.1109/iscas.2015.7168585","title":"Despeckling of synthetic aperture radar images in the contourlet domain using the alpha-stable distribution","display_name":"Despeckling of synthetic aperture radar images in the contourlet domain using the alpha-stable distribution","publication_year":2015,"publication_date":"2015-05-01","ids":{"openalex":"https://openalex.org/W1550006058","doi":"https://doi.org/10.1109/iscas.2015.7168585","mag":"1550006058"},"language":"en","primary_location":{"id":"doi:10.1109/iscas.2015.7168585","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas.2015.7168585","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, Quebec, Canada","Department of Electrical and Computer Engineering, Concordia University,                        Montreal, Quebec, Canada H3G 1M8"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University,                        Montreal, Quebec, 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, Quebec, Canada","Department of Electrical and Computer Engineering, Concordia University,                        Montreal, Quebec, Canada H3G 1M8"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University,                        Montreal, Quebec, 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, Quebec, Canada","Department of Electrical and Computer Engineering, Concordia University,                        Montreal, Quebec, Canada H3G 1M8"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University,                        Montreal, Quebec, 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":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"2","issue":null,"first_page":"121","last_page":"124"},"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9954000115394592,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9922000169754028,"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":0.9498981237411499},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7251142263412476},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.690544843673706},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6756632328033447},{"id":"https://openalex.org/keywords/speckle-noise","display_name":"Speckle noise","score":0.6420974731445312},{"id":"https://openalex.org/keywords/speckle-pattern","display_name":"Speckle pattern","score":0.5980141758918762},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.5811285376548767},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5121088027954102},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.506564736366272},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.48267462849617004},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.47745683789253235},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4720757007598877},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4425254464149475},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24260440468788147},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.22809842228889465},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.13243603706359863},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12690863013267517},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.11723536252975464}],"concepts":[{"id":"https://openalex.org/C20479862","wikidata":"https://www.wikidata.org/wiki/Q5165589","display_name":"Contourlet","level":4,"score":0.9498981237411499},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7251142263412476},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.690544843673706},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6756632328033447},{"id":"https://openalex.org/C180940675","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle noise","level":3,"score":0.6420974731445312},{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.5980141758918762},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.5811285376548767},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5121088027954102},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.506564736366272},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.48267462849617004},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.47745683789253235},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4720757007598877},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4425254464149475},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24260440468788147},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.22809842228889465},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.13243603706359863},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12690863013267517},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.11723536252975464}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscas.2015.7168585","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas.2015.7168585","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":[{"id":"https://metadata.un.org/sdg/11","score":0.41999998688697815,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1484664922","https://openalex.org/W1522590968","https://openalex.org/W1540920731","https://openalex.org/W1602107991","https://openalex.org/W1965445419","https://openalex.org/W1970478368","https://openalex.org/W1970961068","https://openalex.org/W1971103086","https://openalex.org/W1972697625","https://openalex.org/W2022710497","https://openalex.org/W2040558057","https://openalex.org/W2069068791","https://openalex.org/W2096011446","https://openalex.org/W2106219791","https://openalex.org/W2108171046","https://openalex.org/W2122072454","https://openalex.org/W2130219601","https://openalex.org/W2134977633","https://openalex.org/W2158940042","https://openalex.org/W4214806317","https://openalex.org/W6631284433","https://openalex.org/W6632414259","https://openalex.org/W6635873946","https://openalex.org/W6667622725","https://openalex.org/W7075664480"],"related_works":["https://openalex.org/W2065648684","https://openalex.org/W2009383287","https://openalex.org/W2018924981","https://openalex.org/W2042914788","https://openalex.org/W2182190754","https://openalex.org/W4321264664","https://openalex.org/W2055824452","https://openalex.org/W2121688719","https://openalex.org/W2727313114","https://openalex.org/W2016481886"],"abstract_inverted_index":{"Speckle":[0],"reduction":[1],"has":[2],"been":[3],"a":[4,15,39,72,75],"prerequisite":[5],"for":[6,18,42],"many":[7],"SAR":[8,21,97],"image":[9],"processing":[10],"tasks.":[11],"This":[12,66],"work":[13],"presents":[14],"new":[16],"approach":[17],"despeckling":[19,89],"of":[20,46,59,62,86,111,120,122,138,151],"images":[22,106],"in":[23,71],"the":[24,28,35,43,53,60,63,80,87,109,112,123,131,139,152],"contourlet":[25,44,82],"domain":[26],"using":[27,93,103],"alpha-stable":[29,36],"distribution.":[30],"It":[31],"is":[32,68,91],"shown":[33],"that":[34,119,130],"distribution":[37,61],"provides":[38],"good":[40],"fit":[41],"coefficients":[45],"an":[47],"image,":[48],"since":[49],"it":[50,117],"can":[51,134,142],"capture":[52],"large":[54],"peak":[55],"and":[56,95,115,141],"heavy":[57],"tails":[58],"empirical":[64],"data.":[65],"model":[67],"then":[69],"exploited":[70],"Bayesian":[73],"maximum":[74],"posteriori":[76],"estimator":[77],"to":[78,107,149],"restore":[79],"noise-free":[81],"coefficients.":[83],"The":[84,126],"performance":[85,110],"proposed":[88,113,132],"method":[90,133],"evaluated":[92],"synthetically-speckled":[94],"real":[96],"images.":[98],"Simulations":[99],"are":[100],"carried":[101],"out":[102],"synthetically":[104],"speckled":[105],"investigate":[108],"method,":[114],"compare":[116],"with":[118],"some":[121,150],"existing":[124,153],"methods.":[125,154],"experimental":[127],"results":[128],"show":[129],"provide":[135],"better":[136,144],"preservation":[137],"edges":[140],"yield":[143],"visual":[145],"quality":[146],"as":[147],"compared":[148]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
