基于小波子带系数能量的优化权值降噪  

Noise reduction of optimization weight based on energy of wavelet sub-band coefficients

作　　者：王凯[1] 刘甲甲[1] 袁建英[1] 江晓亮[1] 熊鹰[1] 李柏林[1] 

机构地区：[1]西南交通大学机械工程学院,成都610031

出　　处：《计算机应用》2013年第8期2341-2345,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(51275431);四川省科技支撑计划项目(2012GZ0102);四川省科技创新苗子工程资助项目(2012ZZ057;2012ZZ056);西南交通大学牵引动力国家重点实验室自主研究项目(2012TPL_T10)

摘　　要：针对小波阈值降噪中阈值函数选取的关键问题,为解决常规阈值函数存在的不连续性以及估计得到的小波系数存在较大偏差的问题,提出一种在整个小波域都连续的自适应阈值函数。该阈值函数充分考虑了各尺度不同方向子带内小波系数的特征,将不同尺度多个方向的子带系数的能量作为该阈值函数的初始权重因子,采用区间进退法和黄金分割法迭代求解其优化的权值,自适应提高估计的小波系数与分解的小波系数的逼近程度。实验结果表明,该方法在去除噪声的同时保留了图像的边缘和细节信息,在不同的噪声标准差下取得了较高的峰值信噪比(PSNR)。Concerning the key problems of selecting threshold function in wavelet threshold denoising,in order to address the discontinuity of conventional threshold function and large deviation existing in the estimated wavelet coefficients,a continuous adaptive threshold function in the whole wavelet domain was proposed.It fully considered the characteristics of different sub-band coefficients in different scales,and set the energy of sub-band coefficients in different scales as threshold function＇s initial weights.Optimal weights were iteratively solved by using interval advanced-retreat method and golden section method,so as to adaptively improve approximation level between estimated and decomposed wavelet coefficients.The experimental results show that the proposed method can both efficiently reduce noise and simultaneously preserve the edges and details of image,also achieve higher Peak Signal-to-Noise Ratio（PSNR） under different noise standard deviations.

关 键 词：图像去噪 小波变换 阈值去噪 多尺度分解 阈值函数 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]