Construction of Hilbert Transform Pairs of Wavelet Bases and Gabor-Like Transforms
We propose a novel method for constructing Hilbert transform (HT) pairs of wavelet bases based on a fundamental approximation-theoretic characterization of scaling functions-the B-spline factorization theorem. In particular, starting from well-localized scaling functions, we construct HT pairs of bi...
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description | We propose a novel method for constructing Hilbert transform (HT) pairs of wavelet bases based on a fundamental approximation-theoretic characterization of scaling functions-the B-spline factorization theorem. In particular, starting from well-localized scaling functions, we construct HT pairs of biorthogonal wavelet bases of L 2 (R) by relating the corresponding wavelet filters via a discrete form of the continuous HT filter. As a concrete application of this methodology, we identify HT pairs of spline wavelets of a specific flavor, which are then combined to realize a family of complex wavelets that resemble the optimally-localized Gabor function for sufficiently large orders. Analytic wavelets, derived from the complexification of HT wavelet pairs, exhibit a one-sided spectrum. Based on the tensor-product of such analytic wavelets, and, in effect, by appropriately combining four separable biorthogonal wavelet bases of L 2 (R 2 ), we then discuss a methodology for constructing 2-D directional-selective complex wavelets. In particular, analogous to the HT correspondence between the components of the 1-D counterpart, we relate the real and imaginary components of these complex wavelets using a multidimensional extension of the HT-the directional HT. Next, we construct a family of complex spline wavelets that resemble the directional Gabor functions proposed by Daugman. Finally, we present an efficient fast Fourier transform (FFT)-based filterbank algorithm for implementing the associated complex wavelet transform. |
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In particular, starting from well-localized scaling functions, we construct HT pairs of biorthogonal wavelet bases of L 2 (R) by relating the corresponding wavelet filters via a discrete form of the continuous HT filter. As a concrete application of this methodology, we identify HT pairs of spline wavelets of a specific flavor, which are then combined to realize a family of complex wavelets that resemble the optimally-localized Gabor function for sufficiently large orders. Analytic wavelets, derived from the complexification of HT wavelet pairs, exhibit a one-sided spectrum. Based on the tensor-product of such analytic wavelets, and, in effect, by appropriately combining four separable biorthogonal wavelet bases of L 2 (R 2 ), we then discuss a methodology for constructing 2-D directional-selective complex wavelets. In particular, analogous to the HT correspondence between the components of the 1-D counterpart, we relate the real and imaginary components of these complex wavelets using a multidimensional extension of the HT-the directional HT. Next, we construct a family of complex spline wavelets that resemble the directional Gabor functions proposed by Daugman. Finally, we present an efficient fast Fourier transform (FFT)-based filterbank algorithm for implementing the associated complex wavelet transform.</description><identifier>ISSN: 1053-587X</identifier><identifier>EISSN: 1941-0476</identifier><identifier>DOI: 10.1109/TSP.2009.2020767</identifier><identifier>CODEN: ITPRED</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Analytic signal ; Applied sciences ; Approximation ; B-spline multiresolution ; biorthogonal wavelet basis ; Concrete ; Construction ; Continuous wavelet transforms ; Detection, estimation, filtering, equalization, prediction ; directional Hilbert transform ; Discrete wavelet transforms ; dual-tree complex wavelet transform ; Exact sciences and technology ; Fast Fourier transforms ; Filter bank ; Fourier transforms ; Gabor filters ; Gabor function ; Heat treatment ; Hilbert transform ; Information, signal and communications theory ; Mathematical analysis ; Methodology ; Miscellaneous ; Multidimensional systems ; Signal and communications theory ; Signal processing ; Signal, noise ; Spline ; Splines ; Studies ; Telecommunications and information theory ; time-frequency localization ; Transforms ; Wavelet ; Wavelet analysis ; Wavelet transforms</subject><ispartof>IEEE transactions on signal processing, 2009-09, Vol.57 (9), p.3411-3425</ispartof><rights>2009 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2009</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c425t-b9f8b582b41fdc1d100aed1b87f57b20d361f941d573ce2e6b7e2a75067499983</citedby><cites>FETCH-LOGICAL-c425t-b9f8b582b41fdc1d100aed1b87f57b20d361f941d573ce2e6b7e2a75067499983</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4813258$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4813258$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=21829372$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Chaudhury, K.N.</creatorcontrib><creatorcontrib>Unser, M.</creatorcontrib><title>Construction of Hilbert Transform Pairs of Wavelet Bases and Gabor-Like Transforms</title><title>IEEE transactions on signal processing</title><addtitle>TSP</addtitle><description>We propose a novel method for constructing Hilbert transform (HT) pairs of wavelet bases based on a fundamental approximation-theoretic characterization of scaling functions-the B-spline factorization theorem. In particular, starting from well-localized scaling functions, we construct HT pairs of biorthogonal wavelet bases of L 2 (R) by relating the corresponding wavelet filters via a discrete form of the continuous HT filter. As a concrete application of this methodology, we identify HT pairs of spline wavelets of a specific flavor, which are then combined to realize a family of complex wavelets that resemble the optimally-localized Gabor function for sufficiently large orders. Analytic wavelets, derived from the complexification of HT wavelet pairs, exhibit a one-sided spectrum. Based on the tensor-product of such analytic wavelets, and, in effect, by appropriately combining four separable biorthogonal wavelet bases of L 2 (R 2 ), we then discuss a methodology for constructing 2-D directional-selective complex wavelets. In particular, analogous to the HT correspondence between the components of the 1-D counterpart, we relate the real and imaginary components of these complex wavelets using a multidimensional extension of the HT-the directional HT. Next, we construct a family of complex spline wavelets that resemble the directional Gabor functions proposed by Daugman. Finally, we present an efficient fast Fourier transform (FFT)-based filterbank algorithm for implementing the associated complex wavelet transform.</description><subject>Analytic signal</subject><subject>Applied sciences</subject><subject>Approximation</subject><subject>B-spline multiresolution</subject><subject>biorthogonal wavelet basis</subject><subject>Concrete</subject><subject>Construction</subject><subject>Continuous wavelet transforms</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>directional Hilbert transform</subject><subject>Discrete wavelet transforms</subject><subject>dual-tree complex wavelet transform</subject><subject>Exact sciences and technology</subject><subject>Fast Fourier transforms</subject><subject>Filter bank</subject><subject>Fourier transforms</subject><subject>Gabor filters</subject><subject>Gabor function</subject><subject>Heat treatment</subject><subject>Hilbert transform</subject><subject>Information, signal and communications theory</subject><subject>Mathematical analysis</subject><subject>Methodology</subject><subject>Miscellaneous</subject><subject>Multidimensional systems</subject><subject>Signal and communications theory</subject><subject>Signal processing</subject><subject>Signal, noise</subject><subject>Spline</subject><subject>Splines</subject><subject>Studies</subject><subject>Telecommunications and information theory</subject><subject>time-frequency localization</subject><subject>Transforms</subject><subject>Wavelet</subject><subject>Wavelet analysis</subject><subject>Wavelet transforms</subject><issn>1053-587X</issn><issn>1941-0476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp90c9LwzAUB_AiCs7pXfBSBPXUmZ9NctShmzBw6ERvIW1foLNrNGkF_3szNiZ4MIck8D558PJNklOMRhgjdb14no8IQipuBIlc7CUDrBjOEBP5frwjTjMuxdthchTCEiHMmMoHydPYtaHzfdnVrk2dTad1U4Dv0oU3bbDOr9K5qX1Yl17NFzTQpbcmQEhNW6UTUzifzep3-PXhODmwpglwsj2Hycv93WI8zWaPk4fxzSwrGeFdVigrCy5JwbCtSlxhhAxUuJDCclEQVNEc2zhBxQUtgUBeCCBGcJQLppSSdJhcbfp-ePfZQ-j0qg4lNI1pwfVBy2gJjyvKy38lZUoQyWiE53_g0vW-jVNomWNMKWd5RGiDSu9C8GD1h69Xxn9rjPQ6Cx2z0Oss9DaL-ORi29eE0jQ2_lVZh907giVRVJDozjauBoBdmUlMCZf0BxhHkL0</recordid><startdate>20090901</startdate><enddate>20090901</enddate><creator>Chaudhury, K.N.</creator><creator>Unser, M.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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In particular, starting from well-localized scaling functions, we construct HT pairs of biorthogonal wavelet bases of L 2 (R) by relating the corresponding wavelet filters via a discrete form of the continuous HT filter. As a concrete application of this methodology, we identify HT pairs of spline wavelets of a specific flavor, which are then combined to realize a family of complex wavelets that resemble the optimally-localized Gabor function for sufficiently large orders. Analytic wavelets, derived from the complexification of HT wavelet pairs, exhibit a one-sided spectrum. Based on the tensor-product of such analytic wavelets, and, in effect, by appropriately combining four separable biorthogonal wavelet bases of L 2 (R 2 ), we then discuss a methodology for constructing 2-D directional-selective complex wavelets. In particular, analogous to the HT correspondence between the components of the 1-D counterpart, we relate the real and imaginary components of these complex wavelets using a multidimensional extension of the HT-the directional HT. Next, we construct a family of complex spline wavelets that resemble the directional Gabor functions proposed by Daugman. Finally, we present an efficient fast Fourier transform (FFT)-based filterbank algorithm for implementing the associated complex wavelet transform.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TSP.2009.2020767</doi><tpages>15</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Analytic signal Applied sciences Approximation B-spline multiresolution biorthogonal wavelet basis Concrete Construction Continuous wavelet transforms Detection, estimation, filtering, equalization, prediction directional Hilbert transform Discrete wavelet transforms dual-tree complex wavelet transform Exact sciences and technology Fast Fourier transforms Filter bank Fourier transforms Gabor filters Gabor function Heat treatment Hilbert transform Information, signal and communications theory Mathematical analysis Methodology Miscellaneous Multidimensional systems Signal and communications theory Signal processing Signal, noise Spline Splines Studies Telecommunications and information theory time-frequency localization Transforms Wavelet Wavelet analysis Wavelet transforms |
title | Construction of Hilbert Transform Pairs of Wavelet Bases and Gabor-Like Transforms |
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