{"id":"https://openalex.org/W2509240431","doi":"https://doi.org/10.1109/iscas.2016.7527412","title":"A low-complexity MMSE Bayesian estimator for suppression of speckle in SAR images","display_name":"A low-complexity MMSE Bayesian estimator for suppression of speckle in SAR images","publication_year":2016,"publication_date":"2016-05-01","ids":{"openalex":"https://openalex.org/W2509240431","doi":"https://doi.org/10.1109/iscas.2016.7527412","mag":"2509240431"},"language":"en","primary_location":{"id":"doi:10.1109/iscas.2016.7527412","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas.2016.7527412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 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/A5079507190","display_name":"Rafat Damseh","orcid":"https://orcid.org/0000-0001-6797-0448"},"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":"Rafat R. Damseh","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"last","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"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada","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":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1002","last_page":"1005"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9905999898910522,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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":0.987500011920929,"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/speckle-pattern","display_name":"Speckle pattern","score":0.8139582872390747},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6306073665618896},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6185410022735596},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6164031028747559},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.5225082039833069},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4796721935272217},{"id":"https://openalex.org/keywords/speckle-noise","display_name":"Speckle noise","score":0.4737986922264099},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.43399083614349365},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23437273502349854},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.21624824404716492}],"concepts":[{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.8139582872390747},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6306073665618896},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6185410022735596},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6164031028747559},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.5225082039833069},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4796721935272217},{"id":"https://openalex.org/C180940675","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle noise","level":3,"score":0.4737986922264099},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43399083614349365},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23437273502349854},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.21624824404716492}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscas.2016.7527412","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas.2016.7527412","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Symposium on Circuits and Systems (ISCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1602107991","https://openalex.org/W1775729916","https://openalex.org/W1970478368","https://openalex.org/W2010610937","https://openalex.org/W2073354982","https://openalex.org/W2096011446","https://openalex.org/W2106219791","https://openalex.org/W2129553449","https://openalex.org/W2146842127","https://openalex.org/W2147176572","https://openalex.org/W2163599171","https://openalex.org/W2164611927","https://openalex.org/W2167758771","https://openalex.org/W2171031205","https://openalex.org/W3147919432","https://openalex.org/W4300223101"],"related_works":["https://openalex.org/W1964343417","https://openalex.org/W2965843046","https://openalex.org/W1968148863","https://openalex.org/W4313525660","https://openalex.org/W2959574828","https://openalex.org/W2923077656","https://openalex.org/W3191671917","https://openalex.org/W2982947611","https://openalex.org/W1566584116","https://openalex.org/W3118135528"],"abstract_inverted_index":{"In":[0,54],"synthetic":[1],"aperture":[2],"radar":[3],"(SAR)":[4],"images,":[5],"speckle":[6,49,131],"noise":[7,50,132],"reduction":[8,128],"is":[9,37,68,77],"a":[10,22,38,57,95,99,126,134],"crucial":[11],"pre-processing":[12],"step":[13],"for":[14,48,64,83,102],"their":[15],"successful":[16],"interpretation":[17],"and":[18,43,87,139],"thus":[19],"has":[20,44],"drawn":[21],"great":[23],"deal":[24],"of":[25,27,66,73,105,109,119],"attention":[26],"researchers":[28],"in":[29,52,78,89,124,129],"the":[30,74,84,103,106,110,117,120,130,142],"image":[31,143],"processing":[32],"community.":[33],"The":[34,70,113],"Bayesian":[35,61],"estimation":[36,41,62,104],"powerful":[39],"signal":[40],"technique":[42,63,76],"been":[45],"widely":[46],"used":[47],"removal":[51],"images.":[53,112],"this":[55],"work,":[56],"low":[58,136],"complexity":[59],"wavelet-based":[60],"despeckling":[65,122],"images":[67],"developed.":[69],"main":[71],"idea":[72],"proposed":[75,121],"establishing":[79],"suitable":[80],"statistical":[81],"models":[82,92],"wavelet":[85,107],"coefficients":[86,108],"then":[88],"using":[90],"these":[91],"to":[93],"develop":[94],"shrinkage":[96],"function":[97],"with":[98],"low-complexity":[100],"realization":[101],"noise-free":[111],"experimental":[114],"results":[115],"demonstrate":[116],"effectiveness":[118],"scheme":[123],"providing":[125],"significant":[127],"at":[133],"very":[135],"computational":[137],"cost":[138],"simultaneously":[140],"preserving":[141],"details.":[144]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
