摘要
介绍了小波包分解重构的算法及小波阈值消噪的基本原理,并针对软、硬阈值化方法存在的不足,构造了一个新的阈值化函数,该函数采用两个调节参量α和β,通过调整这两个参数,以获得较优的小波系数的阈值估计.经仿真实验表明,新的小波包阈值化方法能对含噪图像进行有效去噪,且较好地克服了原来软、硬阈值存在的振荡和边界模糊等缺陷.统计结果表明,其性能明显优于原有的软、硬阈值化方法,消噪效果较好.图2,表1,参11.
This paper has introduced the algorithm of decompose and compose of the wavelet packets and the theory of wavelet threshold de-noising, A new threshold function was presented in order to overcome the limitations of hard and soft threshold. This new function has two parameters, they can be adjusted properly to produce the best estimations of the wavelet coefficients. The outcome of experiments indicates that the new wavelet packets threshold algorithm can de-noising efficiently and overcome the shortcomings of hard and soft threshold with oscillation and borderline blurred. Statistics shows the improved method can obtain the best de-noising effects by adjusting the parameter values compared with traditional soft and hard threshold function.
出处
《湖南科技大学学报（自然科学版）》
CAS
北大核心
2009年第1期77-80,共4页
Journal of Hunan University of Science And Technology：Natural Science Edition
基金
国家自然科学基金资助项目(60674003)
关键词
小波包
图像去噪
改进阈值函数
峰值信噪比
wavelet packets
image de-noising
improved threshold function
peak signal to noise ratio
