Chaotic Signal Denoising Algorithm Based on Self-Similarity

HUANG Jinwang; LYU Shanxiang; CHEN Yue

doi:10.1049/cje.2021.04.001

Volume 30 Issue 3

May 2021

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HUANG Jinwang, LYU Shanxiang, CHEN Yue, “Chaotic Signal Denoising Algorithm Based on Self-Similarity,” Chinese Journal of Electronics, vol. 30, no. 3, pp. 482-488, 2021, doi: 10.1049/cje.2021.04.001

Citation:

HUANG Jinwang, LYU Shanxiang, CHEN Yue, “Chaotic Signal Denoising Algorithm Based on Self-Similarity,” Chinese Journal of Electronics, vol. 30, no. 3, pp. 482-488, 2021, doi: 10.1049/cje.2021.04.001

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Chaotic Signal Denoising Algorithm Based on Self-Similarity

doi: 10.1049/cje.2021.04.001

1. School of Cyberspace Science, Dongguan University of Technology, Dongguan 523808, China;
2. College of Cyber Security, Jinan University, Guangzhou 510632, China;
3. School of Robotics, The Open University of Guangdong, Guangzhou 510091, China

Funds:

This work is supported by the National Natural Science Foundation of China (No.61872083, No.61902149, No.61932010, No.62032009), the Natural Science Foundation of Guangdong Province (No.2017A030310659, No.2019A1515011123), and the Key Scientific Research Platforms and Projects of Universities in Guangdong Province (No.2018KTSCX344).

Received Date: 2019-07-01

Abstract

Abstract

Inspired by the self-similar fractal properties of chaotic attractors and the heuristics of similarity filtering of images, a novel chaotic signal denoising algorithm is proposed. By grouping the chaotic signal with similar segments, the denoising of one-dimensional input is transformed into a two-dimensional joint filtering problem. Singular value decomposition is performed on the grouped signal segments and the transform coefficients are processed by thresholding to attenuate noise and finally undergo inverse transformation to recover the signal. Because the similar segments in the grouping have good correlation, the two-dimensional transformation of the grouping can obtain a more sparse representation of the original signal compared with the threshold value denoising in the direct one-dimensional transform domain, thereby having better noise suppression effect. Simulation results show that the algorithm can improve the reconstruction accuracy and has better signal-to-noise ratio than existing chaotic signal denoising algorithms.
- Self-similarity,
- Noise reduction,
- Singular value decomposition,
- Chaotic signal

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References(24)

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