EMPIRICAL MODE DECOMPOSITION BASED DENOISING TECHNIQUES

One of the most challenging tasks for which EMD could be usefu l is that of non-parametric signal denoising, an area in which wavelet thresholding has been the dominant technique for many years . In this paper, the major wavelet thresholding principle is used in t he decomposition modes resulting from applying EMD to a signal. We sh ow, that although a direct application of this principle in the E MD case is not feasible, it can appropriately adapted by exploiting the special characteristics of the EMD decomposition modes. In the same manner, inspired by the translation invariant wavelet thre s olding, a similar technique adapted to EMD is developed leading to enh anced denoising performance.

[1]  Toshihisa Tanaka,et al.  A Flexible Method for Envelope Estimation in Empirical Mode Decomposition , 2006, KES.

[2]  Gabriel Rilling,et al.  EMD Equivalent Filter Banks, from Interpretation to Applications , 2005 .

[3]  Jianhua Chen,et al.  The Removal of Wall Components in Doppler Ultrasound Signals by Using the Empirical Mode Decomposition Algorithm , 2007, IEEE Transactions on Biomedical Engineering.

[4]  Norden E. Huang,et al.  STATISTICAL SIGNIFICANCE TEST OF INTRINSIC MODE FUNCTIONS , 2010 .

[5]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[6]  Keikichi Hirose,et al.  Single-Mixture Audio Source Separation by Subspace Decomposition of Hilbert Spectrum , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[7]  Jiwu Huang,et al.  Robust Image Watermarking Based on Multiband Wavelets and Empirical Mode Decomposition , 2007, IEEE Transactions on Image Processing.

[8]  Peiwen Que,et al.  Noise suppression and flaw detection of ultrasonic signals via empirical mode decomposition , 2007 .

[9]  Tang Jing-tian,et al.  Hilbert-Huang Transform for ECG De-Noising , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[10]  Marion Kee,et al.  Analysis , 2004, Machine Translation.

[11]  Steve McLaughlin,et al.  Improved EMD Using Doubly-Iterative Sifting and High Order Spline Interpolation , 2008, EURASIP J. Adv. Signal Process..

[12]  Noel Cressie,et al.  Deterministic/Stochastic Wavelet Decomposition for Recovery of Signal From Noisy Data , 2000, Technometrics.

[13]  Steve McLaughlin,et al.  Investigation and Performance Enhancement of the Empirical Mode Decomposition Method Based on a Heuristic Search Optimization Approach , 2008, IEEE Transactions on Signal Processing.

[14]  Anestis Antoniadis,et al.  Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study , 2001 .

[15]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[16]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[17]  I. Johnstone,et al.  Wavelet Threshold Estimators for Data with Correlated Noise , 1997 .

[18]  Jean-Christophe Cexus,et al.  Denoising via empirical mode decomposition , 2006 .

[19]  Gabriel Rilling,et al.  One or Two Frequencies? The Empirical Mode Decomposition Answers , 2008, IEEE Transactions on Signal Processing.