Adequate determination of a band of wavelet threshold for noise cancellation using particle swarm optimization

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI:10.1109/CEC.2008.4630944

Tsung-Ying Sun, Chan-Cheng Liu, Tsung-Ying Tsai, Sheng-Ta Hsieh

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引用次数: 10

Abstract

Noise reduction problem is addressed by this study. Recently, wavelet thresholding has become popular and has gotten much attention among a number of de-noisy approaches. The most of threshold determination are developed from universal method proposed by Donoho. But, some shortcomings of the determination are caused from several incorrectly estimated factors and the lack of adaptability for whole frequency. By the reason, this paper replaces a universal threshold by multi-thresholds for matching the coefficients of each wavelet segment, and then the band of threshold will be fined by particle swarm optimization (PSO). Because original signals and noise are mutually independent, an objective function of PSO is created to evaluate the second order correlation and high order correlation. In order to confirm the validity and efficiency of the proposed algorithm, several simulations which include four benchmarks with high or low noise degree are designed. Moreover, the performance of proposed algorithm will have compared with that of other existing algorithms.

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利用粒子群优化确定小波阈值以消除噪声

本研究解决了噪声降低问题。近年来，小波阈值法在众多去噪方法中得到了广泛的应用和广泛的关注。大多数阈值的确定都是从Donoho提出的通用方法发展而来的。但是，由于一些估计不正确的因素和对整个频率的适应性不足，导致了该方法的一些缺点。为此，本文用多阈值代替通用阈值来匹配每个小波段的系数，然后通过粒子群优化(PSO)来确定阈值的范围。由于原始信号和噪声是相互独立的，因此建立了粒子群算法的目标函数来评价二阶相关和高阶相关。为了验证所提算法的有效性和有效性，设计了包括高低噪声程度4个基准的仿真。此外，还将本文算法的性能与其他现有算法进行了比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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