JOURNAL ARTICLE

New Denoising Method Based on Empirical Mode Decomposition and Improved Thresholding Function

Wahiba MohguenRaïsEl'hadiBekka

Year: 2017 Journal:   Journal of Physics Conference Series Vol: 787 Pages: 012014-012014   Publisher: IOP Publishing

Abstract

This paper presents a new denoising method called EMD-ITF that was based on Empirical Mode Decomposition (EMD) and the Improved Thresholding Function (ITF) algorithms. EMD was applied to decompose adaptively a noisy signal into intrinsic mode functions (IMFs). Then, all the noisy IMFs were thresholded by applying the improved thresholding function to suppress noise and improve the signal to noise ratio (SNR). The method was tested on simulated and real data and the results were compared to the EMD-Based signal denoising methods using the soft thresholding. The results showed the superior performance of the new EMD-ITF denoising over the traditional approach. The performance were evaluated in terms of SNR in dB, and Mean Square Error (MSE).

Keywords:
Thresholding Hilbert–Huang transform Noise reduction Mean squared error Artificial intelligence Noise (video) Pattern recognition (psychology) SIGNAL (programming language) Computer science Step detection Mathematics Signal-to-noise ratio (imaging) Function (biology) Algorithm White noise Statistics Computer vision Image (mathematics)

Metrics

9
Cited By
0.40
FWCI (Field Weighted Citation Impact)
14
Refs
0.60
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Machine Fault Diagnosis Techniques
Physical Sciences →  Engineering →  Control and Systems Engineering
Image and Signal Denoising Methods
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Structural Health Monitoring Techniques
Physical Sciences →  Engineering →  Civil and Structural Engineering

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