Open AccessΑ Modified EMD-ACWA Denoising Scheme using a Noise-only Model
Author(s) -
I. Tellala,
N. Amardjia,
A. Kesmia
Publication year - 2020
Publication title -
engineering, technology and applied science research/engineering, technology and applied science research
Language(s) - English
Resource type - Journals
eISSN - 2241-4487
pISSN - 1792-8036
DOI - 10.48084/etasr.3406
Subject(s) - hilbert–huang transform , noise reduction , gaussian noise , noise (video) , white noise , thresholding , mathematics , additive white gaussian noise , algorithm , pattern recognition (psychology) , step detection , computer science , artificial intelligence , gaussian filter , filter (signal processing) , statistics , computer vision , image (mathematics)
This paper describes a modified denoising approach combining Empirical Mode Decomposition (EMD) and Adaptive Center-Weighted Average (ACWA) filter. The Intrinsic Mode Functions (IMFs), resulting from the EMD decomposition of a noisy signal, are filtered by the ACWA filter, according to the noise level estimated in each IMF via a noise-only model. The noise levels of IMFs are estimated by the characteristics of fractional Gaussian noise through EMD. It is found that this model provides a better estimation of noise compared to the absolute median deviation of the signal used in the conventional method. The proposed EMD-ACWA scheme is tested on simulation and real data with different white Gaussian noise levels and the results are compared with those obtained by the conventional EMD-ACWA, EMD Interval Thresholding (EMD-IT) and wavelet methods. Test results show that the proposed approach has a superior performance over the other methods considered for comparison.