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应用科学学报 ›› 2009, Vol. 27 ›› Issue (2): 182-186.

• 信号与信息处理 • 上一篇    下一篇

双阈值小波域降噪的微粒群优化

周先国 李开宇   

  1. 南京航空航天大学自动化学院，南京210016
  • 收稿日期:2008-09-22 修回日期:2008-11-27 出版日期:2009-04-01 发布日期:2009-04-01
  • 作者简介:李开宇，副教授，研究方向：数字信号处理、数字图像处理、数据采集、传感器技术，E-mail：lky_401@nuaa.edu.cn

Wavelet De-noising with Double-Threshold Based on Particle Swarm Optimization

  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Received:2008-09-22 Revised:2008-11-27 Online:2009-04-01 Published:2009-04-01

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1239

被引次数

2

摘要：

为了改进滤波效果，提高降噪质量，该文在分析目前被广泛应用的软阈值和硬阈值方法的基础上，提出利用微粒群算法对双阈值进行优化. 采用Donoho提出的固定阈值作为上阈值，而把极大极小原理得到的阈值作为下阈值，再利用微粒群算法进行优化，得到最优双阈值进行降噪. 实验结果表明，该方法在降噪中可有效克服采用硬阈值法引起的伪吉布斯（伪Gibbs）现象和软阈值法导致过度光滑使信号失真等缺点，减少了信号的损失. 在提高信噪比的同时明显减小了均方根误差.

关键词: 小波降噪, 双阈值, 软阈值, 硬阈值, 微粒群算法

Abstract:

To improve the de-noising performance of filters, we present a new method to optimize the two thresholds based on particle swarm optimization (PSO) in the wavelet domain. Having analyzed the widely used soft threshold and hard threshold de-noising methods, we select the Donoho threshold as the upper threshold, and minimax threshold as the lower threshold. The PSO algorithm is used to optimize the double-threshold for the best threshold for de-noising. Simulation results show that the proposed method can overcome the psuedo-Gibbs phenomenon of the hard-thresholding method and excessive smoothness of the signal caused by soft-thresholding. It enhances SNR and reduces RMSE, providing better performance than the traditional methods.

Key words: wavelet de-noising, double-threshold, soft-threshold, hard-threshold, PSO

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