计算机应用 ›› 2013, Vol. 33 ›› Issue (09): 2595-2598.DOI: 10.11772/j.issn.1001-9081.2013.09.2595

• 多媒体处理技术 • 上一篇    下一篇

改进的小波双阈值双因子函数去噪

任重1,2,刘莹2,刘国栋1,黄振1   

  1. 1. 江西科技师范大学 光电子与通信重点实验室,南昌 330038
    2. 南昌大学 机电工程学院,南昌 330031;
  • 收稿日期:2013-01-16 修回日期:2013-02-25 出版日期:2013-09-01 发布日期:2013-10-18
  • 通讯作者: 任重
  • 作者简介:任重(1981-),男,江西南昌人,讲师,博士研究生,主要研究方向:光电子及信号、信息处理;
    刘莹(1957-),女,江西吉水人,教授,博士生导师,主要研究方向:光机电一体化、表面技术与纳米摩擦学;
    刘国栋(1977-),男,江西南昌人,教授,博士,主要研究方向:光学精密工程、信号分析;
    黄振(1974-),男,江西南昌人,副教授,主要研究方向:机电一体化、信号测试。
  • 基金资助:

    国家自然科学基金资助项目;江西省自然科学基金资助项目;江西省教育厅项目;江西省卫生厅项目;江西省科技支撑计划项目

Improved wavelet denoising with dual-threshold and dual-factor function

REN Zhong1,2,LIU Ying2,LIU Guodong1,HUANG Zhen1   

  1. 1. Key Laboratory of Photo-electronic and Communication, Jiangxi Science and Technology Normal University, Nanchang Jiangxi 330038, China
    2. Mechanical and Electrical Engineering College, Nanchang University, Nanchang 330031, China
  • Received:2013-01-16 Revised:2013-02-25 Online:2013-10-18 Published:2013-09-01
  • Contact: REN Zhong
  • Supported by:

    ;Natural Science Foundation of Jiangxi Province of China

摘要: 针对传统的小波阈值函数在阈值处不连续、小波估计系数存在偏差等不足,导致去噪后的信号产生吉布斯振荡、失真和信噪比(SNR)无法提高等问题,提出了一种改进的小波阈值函数去噪方法。与传统的软、硬阈值和半软阈值等函数相比,该函数不仅在阈值处连续,便于运算处理,而且由于双阈值变量和双可变因子的引入,使得该函数既兼容了传统阈值函数的优点,还可以通过调节双阈值和双因子,来提高实际应用的灵活性。为了验证该阈值函数的优越性,通过仿真实验并对比几种小波去噪方法的信噪比和均方根误差,实验结果表明,经本阈值函数去噪后的信号在平滑度和失真度上有较大改善,相比软阈值函数,信噪比提高了22.2%,均方根误差减小了42.6%。

关键词: 小波变换, 阈值函数, 信噪比, 均方根误差, 去噪

Abstract: Since the traditional wavelet threshold functions have some drawbacks such as the non-continuity on the points of threshold, large deviation of estimated wavelet coefficients, Gibbs phenomenon and distortion are generated and Signal-to-Noise Ratio (SNR) can be hardly improved for the denoised signal. To overcome these drawbacks, an improved wavelet threshold function was proposed. Compared with the soft, hard, semi-soft threshold function and others, this function was not only continuous on the points of threshold and more convenient to be processed, but also was compatible with the performances of traditional functions and the practical flexibility was greatly improved via adjusting dual threshold parameters and dual variable factors. To verify this improved function, a series of simulation experiments were performed, the SNR and Root-Mean-Square Error (RMSE) values were compared between different denoising methods. The experimental results demonstrate that the smoothness and distortion are greatly enhanced. Compared with soft function, its SNR increases by 22.2% and its RMSE decreases by 42.6%.

Key words: wavelet transform, threshold function, Signal-To-Noise Ratio (SNR), Root-Mean-Square Error (RMSE), denoising

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