{"id":"https://openalex.org/W2435151267","doi":"https://doi.org/10.1109/siu.2016.7495847","title":"New approaches based on real and complex forms of ripplet-I transform for image analysis","display_name":"New approaches based on real and complex forms of ripplet-I transform for image analysis","publication_year":2016,"publication_date":"2016-05-01","ids":{"openalex":"https://openalex.org/W2435151267","doi":"https://doi.org/10.1109/siu.2016.7495847","mag":"2435151267"},"language":"en","primary_location":{"id":"doi:10.1109/siu.2016.7495847","is_oa":false,"landing_page_url":"https://doi.org/10.1109/siu.2016.7495847","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 24th Signal Processing and Communication Application Conference (SIU)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hdl.handle.net/20.500.12498/8264","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041685747","display_name":"H\u00fcseyin Ya\u015far","orcid":"https://orcid.org/0000-0002-7583-980X"},"institutions":[{"id":"https://openalex.org/I4210121093","display_name":"Ministry of Justice","ror":"https://ror.org/03nrb5p64","country_code":"TR","type":"government","lineage":["https://openalex.org/I4210121093"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Huseyin Yasar","raw_affiliation_strings":["T.C Sa\u011fl\u0131k Bakanl\u0131\u011f\u0131, Ankara, T\u00fcrkiye"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"T.C Sa\u011fl\u0131k Bakanl\u0131\u011f\u0131, Ankara, T\u00fcrkiye","institution_ids":["https://openalex.org/I4210121093"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022115380","display_name":"Murat Ceylan","orcid":"https://orcid.org/0000-0001-6503-9668"},"institutions":[{"id":"https://openalex.org/I4210153056","display_name":"KTO Karatay University","ror":"https://ror.org/054341q84","country_code":"TR","type":"education","lineage":["https://openalex.org/I4210153056"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Murat Ceylan","raw_affiliation_strings":["KTO Karatay \u00dcniversitesi M\u00fchendislik Fak\u00fcltesi, Elektrik-Elektronik M\u00fchendisli\u011fi B\u00f6l\u00fcm\u00fc, Konya, T\u00fcrkiye"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KTO Karatay \u00dcniversitesi M\u00fchendislik Fak\u00fcltesi, Elektrik-Elektronik M\u00fchendisli\u011fi B\u00f6l\u00fcm\u00fc, Konya, T\u00fcrkiye","institution_ids":["https://openalex.org/I4210153056"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.169,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.55647709,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"745","last_page":"748"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":1.0,"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":1.0,"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":0.9998999834060669,"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":0.9955999851226807,"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/s-transform","display_name":"S transform","score":0.6114318370819092},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5559572577476501},{"id":"https://openalex.org/keywords/curvelet","display_name":"Curvelet","score":0.5320889353752136},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.45159977674484253},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4456946849822998},{"id":"https://openalex.org/keywords/discrete-hartley-transform","display_name":"Discrete Hartley transform","score":0.4442831575870514},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.4406622350215912},{"id":"https://openalex.org/keywords/discrete-sine-transform","display_name":"Discrete sine transform","score":0.4363234341144562},{"id":"https://openalex.org/keywords/hartley-transform","display_name":"Hartley transform","score":0.4226984977722168},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.4203202724456787},{"id":"https://openalex.org/keywords/discrete-fourier-transform","display_name":"Discrete Fourier transform (general)","score":0.4198061227798462},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.39886826276779175},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3360256552696228},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2979474663734436},{"id":"https://openalex.org/keywords/fractional-fourier-transform","display_name":"Fractional Fourier transform","score":0.27775701880455017},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.23403888940811157},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.1975221335887909},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.1513807475566864},{"id":"https://openalex.org/keywords/fourier-analysis","display_name":"Fourier analysis","score":0.06866240501403809}],"concepts":[{"id":"https://openalex.org/C99234102","wikidata":"https://www.wikidata.org/wiki/Q7395403","display_name":"S transform","level":5,"score":0.6114318370819092},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5559572577476501},{"id":"https://openalex.org/C131720326","wikidata":"https://www.wikidata.org/wiki/Q5196075","display_name":"Curvelet","level":4,"score":0.5320889353752136},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45159977674484253},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4456946849822998},{"id":"https://openalex.org/C192853989","wikidata":"https://www.wikidata.org/wiki/Q1006531","display_name":"Discrete Hartley transform","level":5,"score":0.4442831575870514},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.4406622350215912},{"id":"https://openalex.org/C167058841","wikidata":"https://www.wikidata.org/wiki/Q971039","display_name":"Discrete sine transform","level":5,"score":0.4363234341144562},{"id":"https://openalex.org/C67757890","wikidata":"https://www.wikidata.org/wiki/Q1567607","display_name":"Hartley transform","level":5,"score":0.4226984977722168},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.4203202724456787},{"id":"https://openalex.org/C57733114","wikidata":"https://www.wikidata.org/wiki/Q1006032","display_name":"Discrete Fourier transform (general)","level":5,"score":0.4198061227798462},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.39886826276779175},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3360256552696228},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2979474663734436},{"id":"https://openalex.org/C76563020","wikidata":"https://www.wikidata.org/wiki/Q4817582","display_name":"Fractional Fourier transform","level":4,"score":0.27775701880455017},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.23403888940811157},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.1975221335887909},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.1513807475566864},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.06866240501403809},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/siu.2016.7495847","is_oa":false,"landing_page_url":"https://doi.org/10.1109/siu.2016.7495847","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 24th Signal Processing and Communication Application Conference (SIU)","raw_type":"proceedings-article"},{"id":"pmh:oai:acikerisim.karatay.edu.tr:20.500.12498/8264","is_oa":true,"landing_page_url":"https://hdl.handle.net/20.500.12498/8264","pdf_url":null,"source":{"id":"https://openalex.org/S7407055354","display_name":"KTO Karatay University Institutional Archive","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"https://dx.doi.org/10.1109/SIU.2016.7495847","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:acikerisim.karatay.edu.tr:20.500.12498/8264","is_oa":true,"landing_page_url":"https://hdl.handle.net/20.500.12498/8264","pdf_url":null,"source":{"id":"https://openalex.org/S7407055354","display_name":"KTO Karatay University Institutional Archive","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"https://dx.doi.org/10.1109/SIU.2016.7495847","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1990968248","https://openalex.org/W2008419427","https://openalex.org/W2014700209","https://openalex.org/W2018184551","https://openalex.org/W2037430052","https://openalex.org/W2075776636","https://openalex.org/W2102188225","https://openalex.org/W2113565757","https://openalex.org/W2114910241","https://openalex.org/W2115528090","https://openalex.org/W2132984323","https://openalex.org/W2156447271","https://openalex.org/W2169298245","https://openalex.org/W2884904720","https://openalex.org/W2971033862"],"related_works":["https://openalex.org/W2086323236","https://openalex.org/W2047825277","https://openalex.org/W2161459641","https://openalex.org/W2903035460","https://openalex.org/W2551275593","https://openalex.org/W2002332886","https://openalex.org/W2317934820","https://openalex.org/W2157838777","https://openalex.org/W2103785533","https://openalex.org/W2373159482"],"abstract_inverted_index":{"The":[0],"multi":[1,24],"resolution":[2,25],"analysis":[3,13],"are":[4],"important":[5],"parts":[6],"of":[7,21,34,61,70,93],"image":[8],"processing.":[9],"Curvelet":[10],"transform":[11,28,37,49,72,95],"is":[12,29],"method":[14],"which":[15],"have":[16],"been":[17,51,57],"using":[18,58],"wide":[19,59],"variety":[20,60],"applications":[22],"in":[23],"analysis.":[26],"Ripplet-I":[27],"defined":[30],"by":[31,38,75,96],"recently":[32],"generalising":[33],"the":[35],"curvelet":[36],"adding":[39],"parameters":[40],"support":[41],"(c)":[42],"and":[43,65,88],"degree":[44],"(d).":[45],"Even":[46],"though":[47],"this":[48,76],"has":[50,56],"found":[52],"out":[53],"recently,":[54],"it":[55],"applications.":[62],"Fast":[63],"discrete":[64,68,87,91,97],"complex":[66,89],"fast":[67,86,90],"versions":[69,92],"ripplet-I":[71,94,98],"were":[73,83],"examined":[74],"study.":[77],"In":[78],"denoising":[79],"application,":[80],"better":[81],"results":[82],"obtained":[84],"with":[85],"transform.":[99]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
