基于非高斯分布和上下文法模型的小波阈值去噪算法

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。

Wavelet threshold denoising via non-Gaussian distribution and context model

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.