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Volumn 39, Issue 3, 2015, Pages 450-486

The monogenic synchrosqueezed wavelet transform: A tool for the decomposition/demodulation of AM-FM images

Author keywords

Directional time frequency image analysis; Monogenic signal; Synchrosqueezing; Wavelet transform

Indexed keywords

FREQUENCY MODULATION; NUMERICAL METHODS; WAVELET DECOMPOSITION; WAVELET TRANSFORMS;

EID: 84942191036     PISSN: 10635203     EISSN: 1096603X     Source Type: Journal    
DOI: 10.1016/j.acha.2014.10.003     Document Type: Article
Times cited : (28)

References (48)
  • 1
    • 0002260696 scopus 로고
    • Computation of component image velocity from local phase information
    • D.J. Fleet, and A.D. Jepson Computation of component image velocity from local phase information Int. J. Comput. Vis. 5 1 1990 77 104
    • (1990) Int. J. Comput. Vis. , vol.5 , Issue.1 , pp. 77-104
    • Fleet, D.J.1    Jepson, A.D.2
  • 2
    • 59649119822 scopus 로고    scopus 로고
    • Analytic estimation of subsample spatial shift using the phases of multidimensional analytic signals
    • A. Basarab, H. Liebgott, and P. Delachartre Analytic estimation of subsample spatial shift using the phases of multidimensional analytic signals IEEE Trans. Image Process. 18 2 2009
    • (2009) IEEE Trans. Image Process. , vol.18 , Issue.2
    • Basarab, A.1    Liebgott, H.2    Delachartre, P.3
  • 6
    • 57149141804 scopus 로고    scopus 로고
    • Texture analysis and segmentation using modulation features, generative models, and weighted curve evolution
    • I. Kokkinos, G. Evangelopoulos, and P. Maragos Texture analysis and segmentation using modulation features, generative models, and weighted curve evolution IEEE Trans. Pattern Anal. Mach. Intell. 31 1 2009 142 157
    • (2009) IEEE Trans. Pattern Anal. Mach. Intell. , vol.31 , Issue.1 , pp. 142-157
    • Kokkinos, I.1    Evangelopoulos, G.2    Maragos, P.3
  • 11
    • 29144478505 scopus 로고    scopus 로고
    • Analytic signals and harmonic measures
    • T. Qian Analytic signals and harmonic measures J. Math. Anal. Appl. 314 2006 526 536
    • (2006) J. Math. Anal. Appl. , vol.314 , pp. 526-536
    • Qian, T.1
  • 14
    • 0031257169 scopus 로고    scopus 로고
    • Characterization of signals by the ridges of their wavelet transforms
    • R.A. Carmona, W.L. Hwang, and B. Torrésani Characterization of signals by the ridges of their wavelet transforms IEEE Trans. Signal Process. 45 10 1997 2586 2590
    • (1997) IEEE Trans. Signal Process. , vol.45 , Issue.10 , pp. 2586-2590
    • Carmona, R.A.1    Hwang, W.L.2    Torrésani, B.3
  • 16
    • 0010313868 scopus 로고    scopus 로고
    • A nonlinear squeezing of the continuous wavelet transform based on auditory nerve models
    • A. Aldroubi, M. Unser, CRC Press
    • I. Daubechies, and S. Maes A nonlinear squeezing of the continuous wavelet transform based on auditory nerve models A. Aldroubi, M. Unser, Wavelets in Medicine and Biology 1996 CRC Press
    • (1996) Wavelets in Medicine and Biology
    • Daubechies, I.1    Maes, S.2
  • 17
    • 0029306339 scopus 로고
    • Improving the readability of time-frequency and time-scale representations by the reassignment method
    • F. Auger, and P. Flandrin Improving the readability of time-frequency and time-scale representations by the reassignment method IEEE Trans. Signal Process. 43 5 1995 1068 1089
    • (1995) IEEE Trans. Signal Process. , vol.43 , Issue.5 , pp. 1068-1089
    • Auger, F.1    Flandrin, P.2
  • 18
    • 5444236478 scopus 로고    scopus 로고
    • The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
    • N.E. Huang, Z. Shen, S.R. Long, S.C. Wu, H.H. Shih, Q. Zheng, N.C. Yen, C.C. Tung, and H.H. Liu The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis Proc. R. Soc. A 454 1998 903 995
    • (1998) Proc. R. Soc. A , vol.454 , pp. 903-995
    • Huang, N.E.1    Shen, Z.2    Long, S.R.3    Wu, S.C.4    Shih, H.H.5    Zheng, Q.6    Yen, N.C.7    Tung, C.C.8    Liu, H.H.9
  • 20
    • 85008018510 scopus 로고    scopus 로고
    • One or two frequencies? the empirical mode decomposition answers
    • G. Rilling, and P. Flandrin One or two frequencies? The empirical mode decomposition answers IEEE Trans. Signal Process. 56 1 2008 85 95
    • (2008) IEEE Trans. Signal Process. , vol.56 , Issue.1 , pp. 85-95
    • Rilling, G.1    Flandrin, P.2
  • 22
    • 78751584911 scopus 로고    scopus 로고
    • Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool
    • I. Daubechies, J. Lu, and H.-T. Wu Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool Appl. Comput. Harmon. Anal. 30 2 2011 243 261
    • (2011) Appl. Comput. Harmon. Anal. , vol.30 , Issue.2 , pp. 243-261
    • Daubechies, I.1    Lu, J.2    Wu, H.-T.3
  • 23
    • 80052631685 scopus 로고    scopus 로고
    • One or two frequencies? the synchrosqueezing answer
    • H.-T. Wu, P. Flandrin, and I. Daubechies One or two frequencies? The synchrosqueezing answer Adv. Adapt. Data Anal. 3 1-2 2011 29 39
    • (2011) Adv. Adapt. Data Anal. , vol.3 , Issue.1-2 , pp. 29-39
    • Wu, H.-T.1    Flandrin, P.2    Daubechies, I.3
  • 24
    • 84867513574 scopus 로고    scopus 로고
    • A new algorithm for multicomponent signal analysis based on synchrosqueezing: With an application to signal sampling and denoising
    • S. Meignen, T. Oberlin, and S. Mclaughlin A new algorithm for multicomponent signal analysis based on synchrosqueezing: with an application to signal sampling and denoising IEEE Trans. Signal Process. 60 11 2012 5787 5798
    • (2012) IEEE Trans. Signal Process. , vol.60 , Issue.11 , pp. 5787-5798
    • Meignen, S.1    Oberlin, T.2    Mclaughlin, S.3
  • 27
    • 77957826217 scopus 로고    scopus 로고
    • Empirical mode decomposition: Applications on signal and image processing
    • J.C. Nunes, and E. Delechelle Empirical mode decomposition: applications on signal and image processing Adv. Adapt. Data Anal. 1 1 2009 125 175
    • (2009) Adv. Adapt. Data Anal. , vol.1 , Issue.1 , pp. 125-175
    • Nunes, J.C.1    Delechelle, E.2
  • 28
    • 77957835093 scopus 로고    scopus 로고
    • Image empirical mode decomposition: A new tool for image processing
    • A. Linderhed Image empirical mode decomposition: a new tool for image processing Adv. Adapt. Data Anal. 1 2 2009 265 294
    • (2009) Adv. Adapt. Data Anal. , vol.1 , Issue.2 , pp. 265-294
    • Linderhed, A.1
  • 29
    • 81355153989 scopus 로고    scopus 로고
    • Intrinsic nonlinear multiscale image decomposition: A 2D empirical mode decomposition-like tool
    • E.H.S. Diop, R. Alexandre, and L. Moisan Intrinsic nonlinear multiscale image decomposition: a 2D empirical mode decomposition-like tool Comput. Vis. Image Underst. 116 2012 102 119
    • (2012) Comput. Vis. Image Underst. , vol.116 , pp. 102-119
    • Diop, E.H.S.1    Alexandre, R.2    Moisan, L.3
  • 30
    • 0000713775 scopus 로고    scopus 로고
    • Natural demodulation of two-dimensional fringe patterns. I. General background of the spiral phase quadrature transform
    • K.G. Larkin, D.J. Bone, and M.A. Oldfield Natural demodulation of two-dimensional fringe patterns. I. General background of the spiral phase quadrature transform J. Opt. Soc. Amer. A 18 8 2001 1862 1870
    • (2001) J. Opt. Soc. Amer. A , vol.18 , Issue.8 , pp. 1862-1870
    • Larkin, K.G.1    Bone, D.J.2    Oldfield, M.A.3
  • 31
    • 0001628440 scopus 로고    scopus 로고
    • Natural demodulation of two-dimensional fringe patterns. II. Stationary phase analysis of the spiral phase quadrature transform
    • K.G. Larkin, D.J. Bone, and M.A. Oldfield Natural demodulation of two-dimensional fringe patterns. II. Stationary phase analysis of the spiral phase quadrature transform J. Opt. Soc. Amer. A 18 8 2001 1871 1881
    • (2001) J. Opt. Soc. Amer. A , vol.18 , Issue.8 , pp. 1871-1881
    • Larkin, K.G.1    Bone, D.J.2    Oldfield, M.A.3
  • 33
    • 58249092009 scopus 로고    scopus 로고
    • Improved bi-dimensional EMD and Hilbert spectrum for the analysis of textures
    • X. Guanlei, W. Xiaotong, and X. Xiaogang Improved bi-dimensional EMD and Hilbert spectrum for the analysis of textures Pattern Recognit. 42 2009 718 734
    • (2009) Pattern Recognit. , vol.42 , pp. 718-734
    • Guanlei, X.1    Xiaotong, W.2    Xiaogang, X.3
  • 35
    • 84957613261 scopus 로고    scopus 로고
    • A novel approach to the 2D analytic signal
    • F. Solina, A. Leonardis, Ljubljana, Slovenia
    • M. Bülow, and G. Sommer A novel approach to the 2D analytic signal F. Solina, A. Leonardis, Proc. CAIP'99 Ljubljana, Slovenia 1999 25 32
    • (1999) Proc. CAIP'99 , pp. 25-32
    • Bülow, M.1    Sommer, G.2
  • 36
    • 84891056364 scopus 로고    scopus 로고
    • Synchrosqueezed wave packet transform for 2d mode decomposition
    • H. Yang, and Y. Ying Synchrosqueezed wave packet transform for 2d mode decomposition SIAM J. Imaging Sci. 6 4 2013 1979 2009
    • (2013) SIAM J. Imaging Sci. , vol.6 , Issue.4 , pp. 1979-2009
    • Yang, H.1    Ying, Y.2
  • 37
    • 84867846115 scopus 로고    scopus 로고
    • Phase derivative of monogenic signals in higher dimensional spaces
    • Y. Yang, T. Qian, and F. Sommen Phase derivative of monogenic signals in higher dimensional spaces Complex Anal. Oper. Theory 6 2012 987 1010
    • (2012) Complex Anal. Oper. Theory , vol.6 , pp. 987-1010
    • Yang, Y.1    Qian, T.2    Sommen, F.3
  • 39
    • 70350607959 scopus 로고    scopus 로고
    • Multiresolution monogenic signal analysis using the Riesz-Laplace wavelet transform
    • M. Unser, and D. Van De Ville Multiresolution monogenic signal analysis using the Riesz-Laplace wavelet transform IEEE Trans. Image Process. 18 11 2009 2402 2418
    • (2009) IEEE Trans. Image Process. , vol.18 , Issue.11 , pp. 2402-2418
    • Unser, M.1    Van De Ville, D.2
  • 41
    • 33947412755 scopus 로고    scopus 로고
    • Multiple multidimensional Morse wavelets
    • S.C. Olhede, and G. Metikas Multiple multidimensional Morse wavelets IEEE Trans. Signal Process. 55 3 2007 921 936
    • (2007) IEEE Trans. Signal Process. , vol.55 , Issue.3 , pp. 921-936
    • Olhede, S.C.1    Metikas, G.2
  • 44
    • 77957598733 scopus 로고    scopus 로고
    • The multi-dimensional ensemble empirical mode decomposition method
    • Z. Wu, N.E. Huang, and X. Chen The multi-dimensional ensemble empirical mode decomposition method Adv. Adapt. Data Anal. 1 03 2009 339 372
    • (2009) Adv. Adapt. Data Anal. , vol.1 , Issue.3 , pp. 339-372
    • Wu, Z.1    Huang, N.E.2    Chen, X.3
  • 45
    • 84871772255 scopus 로고    scopus 로고
    • The synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications
    • G. Thakur, E. Brevdo, N.S. Fučkar, and H.-T. Wu The synchrosqueezing algorithm for time-varying spectral analysis: robustness properties and new paleoclimate applications Signal Process. 93 5 2013 1079 1094
    • (2013) Signal Process. , vol.93 , Issue.5 , pp. 1079-1094
    • Thakur, G.1    Brevdo, E.2    Fučkar, N.S.3    Wu, H.-T.4
  • 46
    • 84900032456 scopus 로고    scopus 로고
    • Non-parametric and adaptive modelling of dynamic periodicity and trend with heteroscedastic and dependent errors
    • Y.-C. Chen, M.-Y. Cheng, and H.-T. Wu Non-parametric and adaptive modelling of dynamic periodicity and trend with heteroscedastic and dependent errors J. R. Stat. Soc. Ser. B Stat. Methodol. 76 3 2014 651 682
    • (2014) J. R. Stat. Soc. Ser. B Stat. Methodol. , vol.76 , Issue.3 , pp. 651-682
    • Chen, Y.-C.1    Cheng, M.-Y.2    Wu, H.-T.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.