基于二元广义正态分布模型的双树复数小波图像去噪  

Image denoising based on bivariate generalized normal model of DTCWT

作　　者：吴宇宁[1] 王美清[1] 

机构地区：[1]福州大学数学与计算机科学学院,福建福州350002

出　　处：《福州大学学报（自然科学版）》2007年第6期840-843,881,共5页Journal of Fuzhou University(Natural Science Edition)

基　　金：福建省自然科学基金资助项目(A0510005)

摘　　要：双树复数小波变换具有平移不变性和多方向选择性,适用于图像去噪.对小波系数统计分布进行建模,提出了一种二元广义正态分布的概率模型.在此先验分布的基础上,通过运用最大后验概率估计方法,从含噪系数中去除高斯噪声.实验表明,该方法不仅在直观视觉上去噪效果明显,在信噪比方面也要优于Bayes-Shrink、W iener2、SureShrink等方法.Complex wavelet transform is suitable for image denoising clue to its characteristics of shift invariance and multi -directional selectivity. A model based on statistical distribution for complex wavelet coefficients is proposed which using the bivariate generalized normal probability density func- tion. Under such prior distribution, MAP （Maximum a Posteriori ） estimator is used to restore the wavelet coefficients from the noisy observations. Experiments show that the proposed method is better than recently published methods, such as BayesShrink, Wiener2 and SureShrink.

关 键 词：正态分布模型 小波变换 贝叶斯统计模型 图像去噪 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]