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

被引:23
|
作者
Clausel, Marianne [1 ]
Oberlin, Thomas [1 ]
Perrier, Valerie [1 ]
机构
[1] Univ Grenoble Alpes, Lab Jean Kuntzmann, CNRS, UMR 5224, F-38041 Grenoble 9, France
关键词
Monogenic signal; Wavelet transform; Directional time-frequency image analysis; Synchrosqueezing; EMPIRICAL MODE DECOMPOSITION; 2-DIMENSIONAL FRINGE PATTERNS; NATURAL DEMODULATION; SIGNALS; ALGORITHM;
D O I
10.1016/j.acha.2014.10.003
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
The synchrosqueezing method aims at decomposing 1D functions into superpositions of a small number of "Intrinsic Modes", supposed to be well separated both in time and frequency. Based on the unidimensional wavelet transform and its reconstruction properties, the synchrosqueezing transform provides a powerful representation of multicomponent signals in the time frequency plane, together with a reconstruction of each mode. In this paper, a bidimensional version of the synchrosqueezing transform is defined, by considering a well-adapted extension of the concept of analytic signal to images: the monogenic signal. We introduce the concept of "Intrinsic Monogenic Mode", that is the bidimensional counterpart of the notion of Intrinsic Mode. We also investigate the properties of its associated Monogenic Wavelet Decomposition. This leads to a natural bivariate extension of the Synchrosqueezed Wavelet Transform, for decomposing and processing multicomponent images. Numerical tests validate the effectiveness of the method on synthetic and real images. (C) 2014 Elsevier Inc. All rights reserved.
引用
收藏
页码:450 / 486
页数:37
相关论文
共 50 条
  • [31] Adaptive maximum windowed likelihood multicomponent AM-FM signal decomposition
    Gazor, S
    Far, RR
    IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2006, 14 (02): : 479 - 491
  • [32] Novel approach to AM-FM decomposition with applications to speech and music analysis
    Sekhar, SC
    Sreenivas, TV
    2004 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL II, PROCEEDINGS: SENSOR ARRAY AND MULTICHANNEL SIGNAL PROCESSING SIGNAL PROCESSING THEORY AND METHODS, 2004, : 753 - 756
  • [33] MULTISCALE AM-FM ANALYSIS OF PNEUMOCONIOSIS X-RAY IMAGES
    Murray, Victor
    Pattichis, Marios S.
    Davis, Herbert
    Barriga, Eduardo S.
    Soliz, Peter
    2009 16TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-6, 2009, : 4201 - +
  • [34] M-mode echocardiography image and video segmentation based on AM-FM demodulation techniques
    Rodríguez, PV
    Pattichis, MS
    Goens, MB
    PROCEEDINGS OF THE 25TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-4: A NEW BEGINNING FOR HUMAN HEALTH, 2003, 25 : 1176 - 1179
  • [35] Projection-based adaptive AM-FM chirp components signal decomposition
    Far, RR
    Gazor, S
    2005 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Vols 1 and 2, 2005, : 517 - 522
  • [36] A supervised approach for the detection of AM-FM signals' interference regions in spectrogram images
    Bruni, Vittoria
    Vitulano, Domenico
    Marconi, Silvia
    IMAGE AND VISION COMPUTING, 2023, 138
  • [37] Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool
    Daubechies, Ingrid
    Lu, Jianfeng
    Wu, Hau-Tieng
    APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS, 2011, 30 (02) : 243 - 261
  • [38] AM-FM demodulation of spectrograms using localized 2D Max-Gabor analysis
    Ezzat, Tony
    Bouvrie, Jake
    Poggio, Tomaso
    2007 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL IV, PTS 1-3, 2007, : 1061 - +
  • [39] Induction Motor Stator Current AM-FM Model and Demodulation Analysis for Planetary Gearbox Fault Diagnosis
    Feng, Zhipeng
    Chen, Xiaowang
    Zuo, Ming J.
    IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2019, 15 (04) : 2386 - 2394
  • [40] Advances in variational image segmentation using AM-FM models: Regularized demodulation and probabilistic cue integration
    Evangelopoulos, G
    Kokkinos, I
    Maragos, P
    VARIATIONAL, GEOMETRIC, AND LEVEL SET METHODS IN COMPUTER VISION, PROCEEDINGS, 2005, 3752 : 121 - 136