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A Particle Swarm Optimization Technique-Based Parametric Wavelet Thresholding Function for Signal Denoising

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Abstract

The determination of threshold and the construction of thresholding function would directly affect the signal denoising quality in wavelet transform denoising techniques. However, some deficiencies exist in the conventional methods, such as fixed threshold value and the inflexible thresholding functions. To overcome the defects of the traditional wavelet thresholding techniques, a modified particle swarm optimization (MPSO) algorithm-based parametric wavelet thresholding approach is proposed for signal denoising. Firstly, a kind of parametric wavelet thresholding function construction method is proposed on the basis of conventional thresholding functions. With mathematical derivation, the properties of the constructed function are proved. Three dynamic adjustment strategies are then employed to modify the PSO algorithm. The mean square error (MSE) between the original signal and the reconstructed signal is minimized by the MPSO algorithm. Finally, the performances of the proposed approach and the existing methods are simulated by denoising four benchmark signals with different noise levels. The simulation results show that the proposed MPSO-based parametric wavelet thresholding can obtain lower MSE, higher signal-to-noise ratio, and noise suppression ratio compared to the other algorithms. Besides, the denoising visual results also indicate the superiority of the proposed approach in terms of the signal denoising capability.

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Acknowledgments

This project was supported by the National Natural Science Foundation of China (Grant No. 61321491) and Natural Science Foundation of Jiangsu Province (Grant No. BK20151451), China. We would like to express our appreciation to the anonymous referees and the associate editor for their valuable comments and suggestions.

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Authors and Affiliations

  1. College of Defense Engineering, PLA University of Science and Technology, Nanjing, 210007, China

    Xun Zhang, Juelong Li, Jianchun Xing, Ping Wang, Qiliang Yang & Can He

  2. Research Center of Coastal Defense Engineering, Beijing, 100841, China

    Juelong Li

  3. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, China

    Qiliang Yang

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  1. Xun Zhang
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  2. Juelong Li
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  3. Jianchun Xing
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  4. Ping Wang
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  6. Can He
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Correspondence to Juelong Li.

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Zhang, X., Li, J., Xing, J. et al. A Particle Swarm Optimization Technique-Based Parametric Wavelet Thresholding Function for Signal Denoising. Circuits Syst Signal Process 36, 247–269 (2017). https://doi.org/10.1007/s00034-016-0303-x

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