{"id":"https://openalex.org/W2753939635","doi":"https://doi.org/10.1109/icip.2017.8296723","title":"Extended conjugate polar fourier transforminconvolution network","display_name":"Extended conjugate polar fourier transforminconvolution network","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2753939635","doi":"https://doi.org/10.1109/icip.2017.8296723","mag":"2753939635"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2017.8296723","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296723","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","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/A5028804463","display_name":"Can Xu","orcid":"https://orcid.org/0000-0001-6769-2468"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Can Xu","raw_affiliation_strings":["Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045342512","display_name":"Wenrui Dai","orcid":"https://orcid.org/0000-0003-2522-5778"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenrui Dai","raw_affiliation_strings":["Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002494284","display_name":"Hongkai Xiong","orcid":"https://orcid.org/0000-0003-4552-0029"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongkai Xiong","raw_affiliation_strings":["Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"19","issue":null,"first_page":"2453","last_page":"2457"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9940000176429749,"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/T11034","display_name":"Digital Filter Design and Implementation","score":0.978600025177002,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/convolution","display_name":"Convolution (computer science)","score":0.5829893946647644},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.5431311726570129},{"id":"https://openalex.org/keywords/filter-bank","display_name":"Filter bank","score":0.5361073017120361},{"id":"https://openalex.org/keywords/discretization","display_name":"Discretization","score":0.5286032557487488},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4810228943824768},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.4783979654312134},{"id":"https://openalex.org/keywords/overlap\u2013add-method","display_name":"Overlap\u2013add method","score":0.4545847177505493},{"id":"https://openalex.org/keywords/discrete-fourier-transform","display_name":"Discrete Fourier transform (general)","score":0.4399263858795166},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.43809065222740173},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4367731213569641},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.41648998856544495},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.3861992657184601},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3848550319671631},{"id":"https://openalex.org/keywords/fourier-analysis","display_name":"Fourier analysis","score":0.29083251953125},{"id":"https://openalex.org/keywords/fractional-fourier-transform","display_name":"Fractional Fourier transform","score":0.2133983075618744},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.10262122750282288},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.07964441180229187},{"id":"https://openalex.org/keywords/pure-mathematics","display_name":"Pure mathematics","score":0.07475754618644714}],"concepts":[{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5829893946647644},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.5431311726570129},{"id":"https://openalex.org/C100515483","wikidata":"https://www.wikidata.org/wiki/Q3268235","display_name":"Filter bank","level":3,"score":0.5361073017120361},{"id":"https://openalex.org/C73000952","wikidata":"https://www.wikidata.org/wiki/Q17007827","display_name":"Discretization","level":2,"score":0.5286032557487488},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4810228943824768},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.4783979654312134},{"id":"https://openalex.org/C181002996","wikidata":"https://www.wikidata.org/wiki/Q1611641","display_name":"Overlap\u2013add method","level":5,"score":0.4545847177505493},{"id":"https://openalex.org/C57733114","wikidata":"https://www.wikidata.org/wiki/Q1006032","display_name":"Discrete Fourier transform (general)","level":5,"score":0.4399263858795166},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.43809065222740173},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4367731213569641},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.41648998856544495},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.3861992657184601},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3848550319671631},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.29083251953125},{"id":"https://openalex.org/C76563020","wikidata":"https://www.wikidata.org/wiki/Q4817582","display_name":"Fractional Fourier transform","level":4,"score":0.2133983075618744},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.10262122750282288},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.07964441180229187},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.07475754618644714},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2017.8296723","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296723","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W228380312","https://openalex.org/W1488667824","https://openalex.org/W2060237834","https://openalex.org/W2069912449","https://openalex.org/W2072072671","https://openalex.org/W2106321064","https://openalex.org/W2116487012","https://openalex.org/W2117853853","https://openalex.org/W2117881455","https://openalex.org/W2132680427","https://openalex.org/W2135978336","https://openalex.org/W2145263492","https://openalex.org/W2161811007"],"related_works":["https://openalex.org/W2267589039","https://openalex.org/W2369791303","https://openalex.org/W4372260258","https://openalex.org/W2133280289","https://openalex.org/W2360069155","https://openalex.org/W2759540840","https://openalex.org/W2169963286","https://openalex.org/W4254230825","https://openalex.org/W2053055950","https://openalex.org/W2162611351"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"an":[3],"extended":[4],"conjugate":[5,94],"polar":[6],"Fourier":[7,55,118],"transform":[8],"(ECPFT),":[9],"to":[10,49,63,72,129],"design":[11,66],"iterated":[12],"radial":[13,104],"filter":[14,19,80],"bank":[15,20,81],"(RFB)":[16],"and":[17,26,70,76,96,103,109],"directional":[18,43],"(DFB)":[21],"convenient":[22],"for":[23,125],"accurate":[24],"multiscale":[25],"multidirectional":[27],"decomposition":[28,73],"in":[29,45,52,74,82,86,100,112],"discretization":[30,126],"over":[31],"a":[32],"convolution":[33,108],"network.":[34],"With":[35],"conjugated":[36],"symmetric":[37],"form,":[38],"ECPFT":[39,88],"would":[40],"convert":[41],"complex":[42],"wavelets":[44],"original":[46],"spatial":[47],"domain":[48,56,119],"real":[50],"ones":[51],"the":[53,65,87,116],"inverse":[54,117],"of":[57,67,93,98,120],"ECPFT.":[58,121],"Furthermore,":[59],"it":[60],"can":[61],"contribute":[62],"changing":[64],"nonseparable":[68],"RFB":[69],"DFB":[71],"scales":[75],"directions":[77],"with":[78],"1-D":[79],"each":[83],"dimension":[84],"separably":[85],"domain.":[89],"The":[90,122],"generated":[91],"properties":[92],"symmetry":[95],"periodicity":[97],"2\u03c0":[99],"both":[101,113],"angular":[102],"dimensions,":[105],"also":[106],"guarantee":[107],"sampling":[110],"operations":[111],"dimensions":[114],"within":[115],"fast":[123],"algorithm":[124],"is":[127],"developed":[128],"reduce":[130],"complexity.":[131]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}