{"id":"https://openalex.org/W2765799658","doi":"https://doi.org/10.23919/eusipco.2017.8081253","title":"Bayesian method with sparsity enforcing prior of dual-tree complex wavelet transform coefficients for X-ray CT image reconstruction","display_name":"Bayesian method with sparsity enforcing prior of dual-tree complex wavelet transform coefficients for X-ray CT image reconstruction","publication_year":2017,"publication_date":"2017-08-01","ids":{"openalex":"https://openalex.org/W2765799658","doi":"https://doi.org/10.23919/eusipco.2017.8081253","mag":"2765799658"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco.2017.8081253","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2017.8081253","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 25th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hal.science/hal-01567875","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108047222","display_name":"Li Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I277688954","display_name":"Universit\u00e9 Paris-Saclay","ror":"https://ror.org/03xjwb503","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954"]},{"id":"https://openalex.org/I4210097418","display_name":"Laboratoire des signaux et syst\u00e8mes","ror":"https://ror.org/00skw9v43","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I277688954","https://openalex.org/I277688954","https://openalex.org/I4210097418","https://openalex.org/I4210107720"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Li Wang","raw_affiliation_strings":["Laboratoire des signaux et syst\u00e8me, Universit\u00e9 Paris Saclay","Laboratoire des signaux et syst\u00e8mes"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Laboratoire des signaux et syst\u00e8me, Universit\u00e9 Paris Saclay","institution_ids":["https://openalex.org/I277688954","https://openalex.org/I4210097418"]},{"raw_affiliation_string":"Laboratoire des signaux et syst\u00e8mes","institution_ids":["https://openalex.org/I4210097418"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041990954","display_name":"Ali Mohammad\u2010Djafari","orcid":"https://orcid.org/0000-0003-0678-7759"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210097418","display_name":"Laboratoire des signaux et syst\u00e8mes","ror":"https://ror.org/00skw9v43","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I277688954","https://openalex.org/I277688954","https://openalex.org/I4210097418","https://openalex.org/I4210107720"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Ali Mohammad-Djafari","raw_affiliation_strings":["CNRS, Laboratoire des signaux et syst\u00e8me, France","Laboratoire des signaux et syst\u00e8mes"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CNRS, Laboratoire des signaux et syst\u00e8me, France","institution_ids":["https://openalex.org/I4210097418","https://openalex.org/I1294671590"]},{"raw_affiliation_string":"Laboratoire des signaux et syst\u00e8mes","institution_ids":["https://openalex.org/I4210097418"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082573179","display_name":"Nicolas Gac","orcid":"https://orcid.org/0000-0001-6981-0368"},"institutions":[{"id":"https://openalex.org/I277688954","display_name":"Universit\u00e9 Paris-Saclay","ror":"https://ror.org/03xjwb503","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954"]},{"id":"https://openalex.org/I4210097418","display_name":"Laboratoire des signaux et syst\u00e8mes","ror":"https://ror.org/00skw9v43","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I277688954","https://openalex.org/I277688954","https://openalex.org/I4210097418","https://openalex.org/I4210107720"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Nicolas Gac","raw_affiliation_strings":["Laboratoire des signaux et syst\u00e8me, Universit\u00e9 Paris Saclay","Laboratoire des signaux et syst\u00e8mes"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Laboratoire des signaux et syst\u00e8me, Universit\u00e9 Paris Saclay","institution_ids":["https://openalex.org/I277688954","https://openalex.org/I4210097418"]},{"raw_affiliation_string":"Laboratoire des signaux et syst\u00e8mes","institution_ids":["https://openalex.org/I4210097418"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.14940783,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"31","issue":null,"first_page":"478","last_page":"482"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9988999962806702,"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":0.9988999962806702,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9975000023841858,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/complex-wavelet-transform","display_name":"Complex wavelet transform","score":0.886074423789978},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.6169288158416748},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.6012362241744995},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5790178179740906},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5749047994613647},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.572919487953186},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.5524031519889832},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5085538625717163},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5068219304084778},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49997639656066895},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.45956000685691833},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.42906278371810913},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.42676016688346863},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3684256672859192},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3540753126144409},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.2659904956817627}],"concepts":[{"id":"https://openalex.org/C2777885455","wikidata":"https://www.wikidata.org/wiki/Q5156615","display_name":"Complex wavelet transform","level":5,"score":0.886074423789978},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.6169288158416748},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.6012362241744995},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5790178179740906},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5749047994613647},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.572919487953186},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.5524031519889832},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5085538625717163},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5068219304084778},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49997639656066895},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.45956000685691833},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.42906278371810913},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42676016688346863},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3684256672859192},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3540753126144409},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.2659904956817627},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.23919/eusipco.2017.8081253","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2017.8081253","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 25th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-01567875v1","is_oa":true,"landing_page_url":"https://hal.science/hal-01567875","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"25th European Signal Processing Conference (EUSIPCO 2017), Aug 2017, Kos island, Greece. ⟨10.23919/eusipco.2017.8081253⟩","raw_type":"Conference papers"}],"best_oa_location":{"id":"pmh:oai:HAL:hal-01567875v1","is_oa":true,"landing_page_url":"https://hal.science/hal-01567875","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"25th European Signal Processing Conference (EUSIPCO 2017), Aug 2017, Kos island, Greece. ⟨10.23919/eusipco.2017.8081253⟩","raw_type":"Conference papers"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.4300000071525574}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W83592706","https://openalex.org/W1582614941","https://openalex.org/W1631469952","https://openalex.org/W1985588167","https://openalex.org/W1993102688","https://openalex.org/W1995758426","https://openalex.org/W2000721204","https://openalex.org/W2000771799","https://openalex.org/W2005490094","https://openalex.org/W2018332268","https://openalex.org/W2032322171","https://openalex.org/W2094072727","https://openalex.org/W2094366314","https://openalex.org/W2099810224","https://openalex.org/W2101946599","https://openalex.org/W2116641053","https://openalex.org/W2142058898","https://openalex.org/W2154130617","https://openalex.org/W2165956501","https://openalex.org/W2177031359","https://openalex.org/W2404009085","https://openalex.org/W2407641597","https://openalex.org/W4250589301","https://openalex.org/W6685648643"],"related_works":["https://openalex.org/W2158224665","https://openalex.org/W2364423108","https://openalex.org/W2896778670","https://openalex.org/W2547029902","https://openalex.org/W2077021924","https://openalex.org/W1517207589","https://openalex.org/W2009554098","https://openalex.org/W2158530844","https://openalex.org/W2158757538","https://openalex.org/W2147252176"],"abstract_inverted_index":{"In":[0],"this":[1,63],"paper,":[2],"a":[3,7],"Bayesian":[4,64],"method":[5,80],"with":[6,67],"hierarchical":[8],"sparsity":[9],"enforcing":[10],"prior":[11,37],"model":[12,23,38],"for":[13,26],"Dual-Tree":[14],"Complex":[15],"Wavelet":[16],"Transform":[17],"(DT-CWT)":[18],"coefficients":[19],"is":[20,24,39,60,91],"proposed.":[21],"This":[22],"used":[25,40,61],"X-ray":[27],"Computed":[28],"Tomography":[29],"(CT)":[30],"image":[31],"reconstruction.":[32],"A":[33,56],"generalized":[34],"Student-t":[35],"distributed":[36],"to":[41],"enforce":[42],"the":[43,47,51,68,78,89],"sparse":[44],"structure":[45],"of":[46,50],"DT-CWT":[48],"coefficient":[49],"image.":[52],"The":[53],"joint":[54],"Maximum":[55],"Posterior":[57],"algorithm":[58],"(JMAP)":[59],"in":[62],"context.":[65],"Comparisons":[66],"conventional":[69],"and":[70,84],"other":[71],"state-of-the-art":[72],"methods":[73],"are":[74],"presented,":[75],"showing":[76],"that":[77],"proposed":[79],"gives":[81],"more":[82],"accurate":[83],"robust":[85],"reconstruction":[86],"results":[87],"while":[88],"dataset":[90],"insufficient.":[92]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}