{"id":"https://openalex.org/W2055157212","doi":"https://doi.org/10.1137/100803924","title":"Directional Multiscale Amplitude and Phase Decomposition by the Monogenic Curvelet Transform","display_name":"Directional Multiscale Amplitude and Phase Decomposition by the Monogenic Curvelet Transform","publication_year":2011,"publication_date":"2011-01-01","ids":{"openalex":"https://openalex.org/W2055157212","doi":"https://doi.org/10.1137/100803924","mag":"2055157212"},"language":"en","primary_location":{"id":"doi:10.1137/100803924","is_oa":false,"landing_page_url":"https://doi.org/10.1137/100803924","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/A5015529161","display_name":"Martin Storath","orcid":"https://orcid.org/0000-0003-1427-0776"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Martin Storath","raw_affiliation_strings":["storath@ma.tum.de#TAB#"],"affiliations":[{"raw_affiliation_string":"storath@ma.tum.de#TAB#","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5015529161"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.1769,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.91387028,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"4","issue":"1","first_page":"57","last_page":"78"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11245","display_name":"Advanced Numerical Analysis Techniques","score":0.9958000183105469,"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"}},"topics":[{"id":"https://openalex.org/T11245","display_name":"Advanced Numerical Analysis Techniques","score":0.9958000183105469,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9954000115394592,"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/T11210","display_name":"Mathematical Analysis and Transform Methods","score":0.9900000095367432,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/curvelet","display_name":"Curvelet","score":0.9580532312393188},{"id":"https://openalex.org/keywords/s-transform","display_name":"S transform","score":0.7352772355079651},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.645892858505249},{"id":"https://openalex.org/keywords/hilbert-transform","display_name":"Hilbert transform","score":0.6137241125106812},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5417457818984985},{"id":"https://openalex.org/keywords/continuous-wavelet-transform","display_name":"Continuous wavelet transform","score":0.5288435220718384},{"id":"https://openalex.org/keywords/harmonic-wavelet-transform","display_name":"Harmonic wavelet transform","score":0.5264863967895508},{"id":"https://openalex.org/keywords/constant-q-transform","display_name":"Constant Q transform","score":0.5220910310745239},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.46630704402923584},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.4195531904697418},{"id":"https://openalex.org/keywords/phase","display_name":"Phase (matter)","score":0.4158173203468323},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.37041109800338745},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3547484874725342},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3374619483947754},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.28849998116493225},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.2692663073539734},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.16676867008209229},{"id":"https://openalex.org/keywords/spectral-density","display_name":"Spectral density","score":0.10227259993553162},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09994041919708252}],"concepts":[{"id":"https://openalex.org/C131720326","wikidata":"https://www.wikidata.org/wiki/Q5196075","display_name":"Curvelet","level":4,"score":0.9580532312393188},{"id":"https://openalex.org/C99234102","wikidata":"https://www.wikidata.org/wiki/Q7395403","display_name":"S transform","level":5,"score":0.7352772355079651},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.645892858505249},{"id":"https://openalex.org/C28799612","wikidata":"https://www.wikidata.org/wiki/Q685437","display_name":"Hilbert transform","level":3,"score":0.6137241125106812},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5417457818984985},{"id":"https://openalex.org/C95722684","wikidata":"https://www.wikidata.org/wiki/Q2622756","display_name":"Continuous wavelet transform","level":5,"score":0.5288435220718384},{"id":"https://openalex.org/C1109138","wikidata":"https://www.wikidata.org/wiki/Q3280930","display_name":"Harmonic wavelet transform","level":5,"score":0.5264863967895508},{"id":"https://openalex.org/C153705960","wikidata":"https://www.wikidata.org/wiki/Q5163634","display_name":"Constant Q transform","level":5,"score":0.5220910310745239},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.46630704402923584},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.4195531904697418},{"id":"https://openalex.org/C44280652","wikidata":"https://www.wikidata.org/wiki/Q104837","display_name":"Phase (matter)","level":2,"score":0.4158173203468323},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.37041109800338745},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3547484874725342},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3374619483947754},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.28849998116493225},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2692663073539734},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.16676867008209229},{"id":"https://openalex.org/C168110828","wikidata":"https://www.wikidata.org/wiki/Q1331626","display_name":"Spectral density","level":2,"score":0.10227259993553162},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09994041919708252},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1137/100803924","is_oa":false,"landing_page_url":"https://doi.org/10.1137/100803924","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W228380312","https://openalex.org/W1488877410","https://openalex.org/W1520309894","https://openalex.org/W1762506961","https://openalex.org/W2018332268","https://openalex.org/W2033943996","https://openalex.org/W2038181481","https://openalex.org/W2053276035","https://openalex.org/W2069912449","https://openalex.org/W2095553100","https://openalex.org/W2097854005","https://openalex.org/W2115528090","https://openalex.org/W2115755118","https://openalex.org/W2119445186","https://openalex.org/W2126043778","https://openalex.org/W2134429331","https://openalex.org/W2135269154","https://openalex.org/W2137584359","https://openalex.org/W2144506334","https://openalex.org/W2147497470","https://openalex.org/W2149548037","https://openalex.org/W4210381520"],"related_works":["https://openalex.org/W2381453479","https://openalex.org/W1843493412","https://openalex.org/W2095553100","https://openalex.org/W2055157212","https://openalex.org/W2147816597","https://openalex.org/W2013228134","https://openalex.org/W2433457936","https://openalex.org/W2995883449","https://openalex.org/W2070232159","https://openalex.org/W2137702241"],"abstract_inverted_index":{"We":[0,13,39,123],"reconsider":[1],"the":[2,16,20,23,32,35,42,46,54,57,66,73,80,84,90,94,100,107,117,125,135],"continuous":[3],"curvelet":[4,21,68,86],"transform":[5,71,87,102,118],"from":[6],"a":[7,62,112],"signal":[8],"processing":[9],"point":[10],"of":[11,19,34,56,116,127,137],"view.":[12],"show":[14],"that":[15,76],"analyzing":[17],"elements":[18],"transform,":[22,64],"curvelets,":[24,48],"can":[25],"be":[26],"understood":[27],"as":[28],"analytic":[29,51],"signals":[30,52],"in":[31,53],"sense":[33,55],"partial":[36],"Hilbert":[37],"transform.":[38,59,69,97],"then":[40],"generalize":[41],"usual":[43,85],"curvelets":[44],"by":[45],"monogenic":[47,67,95],"which":[49],"are":[50],"Riesz":[58],"They":[60],"yield":[61],"new":[63,101,129],"called":[65],"This":[70],"has":[72],"useful":[74],"property":[75],"it":[77],"behaves":[78],"at":[79,89,106],"fine":[81,108],"scales":[82,92,109],"like":[83,93],"and":[88,110],"coarse":[91],"wavelet":[96],"In":[98],"particular,":[99],"is":[103],"highly":[104],"anisotropic":[105],"yields":[111],"well-interpretable":[113],"amplitude/phase":[114,132],"decomposition":[115,133],"coefficients":[119],"over":[120],"all":[121],"scales.":[122],"illustrate":[124],"advantage":[126],"this":[128],"directional":[130,138],"multiscale":[131],"for":[134],"estimation":[136],"regularity.":[139]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":5},{"year":2012,"cited_by_count":2}],"updated_date":"2026-03-05T09:29:38.588285","created_date":"2025-10-10T00:00:00"}
