A new denoising approach based on EMD

This paper introduces a new approach based on Empirical Mode Decomposition (EMD) for explicitly denoising signals. The EMD decomposes a noisy signal into several intrinsic mode functions(IMFs), and the estimated signal is reconstructed by using the processed IMFs. In this paper, a piecewise EMD thresholding approach for denoising signal with strong noise is proposed. Simulation results show that the proposed approach has good performance, especially in the cases where the noise is very strong.

[1]  D. L. Donoho,et al.  Ideal spacial adaptation via wavelet shrinkage , 1994 .

[2]  Steve McLaughlin,et al.  Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding , 2009, IEEE Transactions on Signal Processing.

[3]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[4]  G. Tsolis,et al.  Signal Denoising Using Empirical Mode Decomposition and Higher Order Statistics , 2011 .

[5]  A. Boudraa,et al.  EMD-Based Signal Noise Reduction , 2005 .

[6]  N. Huang,et al.  A study of the characteristics of white noise using the empirical mode decomposition method , 2004, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

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

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

[9]  V. A. Protopopescu,et al.  Empirical Mode Decomposition Technique With Conditional Mutual Information for Denoising Operational Sensor Data , 2011, IEEE Sensors Journal.

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

[11]  I. Johnstone,et al.  Adapting to Unknown Smoothness via Wavelet Shrinkage , 1995 .

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