{"id":"https://openalex.org/W2039741756","doi":"https://doi.org/10.1137/080720796","title":"Image Deconvolution Using a General Ridgelet and Curvelet Domain","display_name":"Image Deconvolution Using a General Ridgelet and Curvelet Domain","publication_year":2009,"publication_date":"2009-01-01","ids":{"openalex":"https://openalex.org/W2039741756","doi":"https://doi.org/10.1137/080720796","mag":"2039741756"},"language":"en","primary_location":{"id":"doi:10.1137/080720796","is_oa":false,"landing_page_url":"https://doi.org/10.1137/080720796","pdf_url":null,"source":{"id":"https://openalex.org/S152600803","display_name":"SIAM Journal on Imaging Sciences","issn_l":"1936-4954","issn":["1936-4954"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM Journal on Imaging Sciences","raw_type":"journal-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/A5088245220","display_name":"Glenn R. Easley","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Glenn R. Easley","raw_affiliation_strings":["geasley@sysplan.com#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"geasley@sysplan.com#TAB#","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110162620","display_name":"Dennis M. Healy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dennis M. Healy,","raw_affiliation_strings":["dhealy@math.umd.edu#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"dhealy@math.umd.edu#TAB#","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003745132","display_name":"Carlos A. Berenstein","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Carlos A. Berenstein","raw_affiliation_strings":["carlos@glue.umd.edu#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"carlos@glue.umd.edu#TAB#","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.657,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.70592584,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"2","issue":"1","first_page":"253","last_page":"283"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9994999766349792,"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":0.9994999766349792,"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/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9965000152587891,"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/deconvolution","display_name":"Deconvolution","score":0.9172419309616089},{"id":"https://openalex.org/keywords/blind-deconvolution","display_name":"Blind deconvolution","score":0.7871556878089905},{"id":"https://openalex.org/keywords/curvelet","display_name":"Curvelet","score":0.767707347869873},{"id":"https://openalex.org/keywords/inversion","display_name":"Inversion (geology)","score":0.6775556802749634},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.6225460171699524},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5849360823631287},{"id":"https://openalex.org/keywords/wiener-deconvolution","display_name":"Wiener deconvolution","score":0.5134859681129456},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.49598655104637146},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4496057331562042},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3906041085720062},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3672385811805725},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.27070608735084534}],"concepts":[{"id":"https://openalex.org/C174576160","wikidata":"https://www.wikidata.org/wiki/Q1183700","display_name":"Deconvolution","level":2,"score":0.9172419309616089},{"id":"https://openalex.org/C30044814","wikidata":"https://www.wikidata.org/wiki/Q11334452","display_name":"Blind deconvolution","level":3,"score":0.7871556878089905},{"id":"https://openalex.org/C131720326","wikidata":"https://www.wikidata.org/wiki/Q5196075","display_name":"Curvelet","level":4,"score":0.767707347869873},{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.6775556802749634},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.6225460171699524},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5849360823631287},{"id":"https://openalex.org/C15652857","wikidata":"https://www.wikidata.org/wiki/Q599016","display_name":"Wiener deconvolution","level":4,"score":0.5134859681129456},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49598655104637146},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4496057331562042},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3906041085720062},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3672385811805725},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.27070608735084534},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C109007969","wikidata":"https://www.wikidata.org/wiki/Q749565","display_name":"Structural basin","level":2,"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":1,"locations":[{"id":"doi:10.1137/080720796","is_oa":false,"landing_page_url":"https://doi.org/10.1137/080720796","pdf_url":null,"source":{"id":"https://openalex.org/S152600803","display_name":"SIAM Journal on Imaging Sciences","issn_l":"1936-4954","issn":["1936-4954"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM Journal on Imaging Sciences","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W158674010","https://openalex.org/W1551656178","https://openalex.org/W1586402789","https://openalex.org/W1885689559","https://openalex.org/W1965570176","https://openalex.org/W1975780894","https://openalex.org/W1990381576","https://openalex.org/W1995777573","https://openalex.org/W2019787708","https://openalex.org/W2029026810","https://openalex.org/W2049557998","https://openalex.org/W2050001231","https://openalex.org/W2050880896","https://openalex.org/W2053189442","https://openalex.org/W2058583833","https://openalex.org/W2058810548","https://openalex.org/W2066462711","https://openalex.org/W2069912449","https://openalex.org/W2079756223","https://openalex.org/W2086600055","https://openalex.org/W2091540466","https://openalex.org/W2092543127","https://openalex.org/W2099048931","https://openalex.org/W2100115174","https://openalex.org/W2101882738","https://openalex.org/W2103559027","https://openalex.org/W2105282667","https://openalex.org/W2110104525","https://openalex.org/W2115755118","https://openalex.org/W2124331477","https://openalex.org/W2127731450","https://openalex.org/W2132680427","https://openalex.org/W2139673667","https://openalex.org/W2140064412","https://openalex.org/W2148593155","https://openalex.org/W2148688551","https://openalex.org/W2150350689","https://openalex.org/W2155893732","https://openalex.org/W2170407317"],"related_works":["https://openalex.org/W4242983818","https://openalex.org/W2059639161","https://openalex.org/W584962943","https://openalex.org/W2059641814","https://openalex.org/W2348457039","https://openalex.org/W2141591465","https://openalex.org/W4321635651","https://openalex.org/W2139898842","https://openalex.org/W2589098082","https://openalex.org/W2155513095"],"abstract_inverted_index":{"We":[0,57,91],"carry":[1],"out":[2,97],"deconvolution":[3,37,134,139,154],"by":[4,62],"transforming":[5],"the":[6,23,49,54],"data":[7],"into":[8],"a":[9,68,87,120,128],"new":[10,60,130],"general":[11],"discrete":[12],"Radon":[13],"domain":[14],"that":[15,136,145],"can":[16,34,148],"handle":[17],"any":[18],"assumed":[19],"boundary":[20],"conditions":[21],"for":[22,53,95,132],"associated":[24,30],"matrix":[25,45,50],"inversion":[26,46,51,72],"problem.":[27],"For":[28],"each":[29],"component":[31],"(projection),":[32],"one":[33],"then":[35,92,111],"apply":[36],"routines":[38],"to":[39,81,101,115],"smaller":[40],"(and":[41],"possibly":[42],"better)":[43],"conditioned":[44],"problems":[47],"than":[48,152],"problem":[52],"entire":[55],"image.":[56],"demonstrate":[58],"this":[59,98],"scheme":[61,99],"adaptively":[63],"deconvolving":[64],"these":[65],"components":[66],"using":[67],"combination":[69],"of":[70],"regularized":[71],"and":[73,113],"wavelet":[74],"filtering":[75],"techniques.":[76,155],"This":[77],"procedure":[78],"allows":[79],"us":[80],"provide":[82,102],"image":[83],"estimates":[84,103],"based":[85,104],"on":[86,105],"generalized":[88,106],"ridgelet":[89],"frame.":[90],"devise":[93],"methods":[94,147],"carrying":[96],"locally":[100],"multiscaled":[107],"ridgelets":[108],"which":[109],"are":[110],"filtered":[112],"combined":[114],"form":[116],"an":[117],"estimate":[118],"from":[119],"curvelet-like":[121],"domain.":[122],"The":[123],"techniques":[124],"presented":[125],"here":[126],"suggest":[127],"whole":[129],"paradigm":[131],"developing":[133],"algorithms":[135],"incorporate":[137],"leading":[138],"schemes.":[140],"Various":[141],"experimental":[142],"results":[143],"show":[144],"our":[146],"perform":[149],"significantly":[150],"better":[151],"standard":[153]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
