基于非高斯分布和上下文法模型的小波阈值去噪算法  

Wavelet threshold denoising via non-Gaussian distribution and context model

作　　者：杨黎[1] 庄成三[1] 

机构地区：[1]四川大学计算机学院,四川成都610065

出　　处：《计算机应用》2005年第5期1096-1098,1101,共4页journal of Computer Applications

摘　　要：提出了一种新的空间自适应小波阈值去噪算法,该算法是基于非高斯二元分布的贝叶斯统计模型和上下文法模型。非高斯二元分布由两个变元和一个参数组成,能够完全体现小波系数之间相关性,这是广义高斯分布所不能体现的特性。上下文法模型是图像编码技术,用来求取小波系数的方差。试验数据显示该算法不仅在直观视觉上去噪效果明显,而且在信噪比方面也要优于SureShrink、BayesShrink、Wiener2等方法。A new spatial adaptive wavelet threshold denoising method was presented, which was based on a non-Gaussian bivariate distribution and context model for image denoising inspired by image coding. The dependency between coefficients and their parents was carefully studied and a new distribution model composed of two variables and a free parameter was proposed. Context model is the core method in image coding and is applied in this project to choose the spatial adaptive threshold derived in a Bayesian framework. Experiment results show that this new method outperforms the best of the recently published methods, such as SureShrink, Wiener2, and BayesShrink.

关 键 词：小波阈值 贝叶斯统计模型 上下文法模型 非高斯二元分布 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]