• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2013, Vol. 35 ›› Issue (8): 114-119.

• 论文 • 上一篇    下一篇

一类新的小波收缩阈值函数

何希平1,2,杨劲2,3   

  1. (1.重庆工商大学电子商务及供应链系统重庆市重点实验室,重庆 400067;
    2.重庆工商大学计算机科学与信息工程学院,重庆 400067;
    3.重庆市科学技术研究院科技检测中心, 重庆 400123)
  • 收稿日期:2012-05-01 修回日期:2012-08-13 出版日期:2013-08-25 发布日期:2013-08-25
  • 基金资助:

    重庆市科委科技攻关资助项目(CSTC2011GGB0023);电子商务及供应链系统重庆市重点实验室专项基金资助项目(2012ECSC0208)

A novel type of threshold functions for wavelet shrinkage  

HE Xiping1,2,YANG Jin2,3   

  1. (1.Chongqing Key Laboratory of Electronic Commerce & Supply Chain,
    Chongqing Technology and Business University,Chongqing 400067;
    2.School of Computer Science and Information Engineering,Chongqing Technology and Business University,Chongqing 400067;
    3.Scientific and Technological Testing Center,Chongqing Academy of Science and Technology,Chongqing 400123,China)
  • Received:2012-05-01 Revised:2012-08-13 Online:2013-08-25 Published:2013-08-25

摘要:

为克服传统的小波收缩硬阈值函数不连续,软阈值函数既不光滑又使小波系数估计与噪声信号小波系数间存在固定偏差的不足,提出了一类新的含可变参数的阈值函数,并分析了不同参数对信号收缩的影响。该阈值函数不但连续,而且任意阶可导,表达式简单易于计算,便于进行各种数学处理,同时它综合了软、硬阈值函数在去噪处理上的优良特性,还具有软、硬阈值函数不可比拟的灵活性,这些优点为信号的小波自适应阈值去噪提供了方便。仿真对比实验表明,小波的新阈值函数收缩去噪结果,无论是直观效果,还是在均方误差、信噪比等统计特征方面均优于所对比的小波阈值收缩方法。

关键词: 小波收缩, 阈值函数, 均方误差, 信噪比, 峰值信噪比

Abstract:

A novel type of threshold functions with a variable parameter is proposed based on the wavelet shrinkage architecture presented by Donoho  D L and Johnstone I M, and the effect of different parameter on signal shrinkage is also analyzed. This new type of threshold functions has many advantages over the soft and hard threshold functions. It is continuous and simple in expression, and has any order derivative which makes some kinds of mathematical processing very simple and convenient. It also overcomes the shortcomings of the soft threshold method where there is an invariable dispersion between the estimated wavelet coefficients and the decomposed wavelet coefficients. Further more, the new type of threshold functions is more flexible than the softthreshold and hardthreshold functions and integrates their excellence features. All these advantages make it possible to construct an adaptive signal denoising algorithm. Comparative experiment results show that the wavelet shrinkage method adopting the new threshold function is more capable of eliminating the noise from the signal, and results in less MSE and higher SNR than the traditional method adopting hard and soft threshold function.

Key words: wavelet shrinkage;threshold function;MSE;SNR;PSNR