Journal of Computer Applications ›› 2005, Vol. 25 ›› Issue (05): 1096-1098.DOI: 10.3724/SP.J.1087.2005.1096
• Graphics, image and multimedia • Previous Articles Next Articles
YANG Li, ZHUANG Cheng-san
Online:
Published:
杨黎,庄成三
Abstract: 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.
Key words: wavelet threshold, Bayes statistics, context model, non-Gaussian bivariate distribution
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。
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
CLC Number:
TP391.41
YANG Li, ZHUANG Cheng-san. Wavelet threshold denoising via non-Gaussian distribution and context model[J]. Journal of Computer Applications, 2005, 25(05): 1096-1098.
杨黎,庄成三. 基于非高斯分布和上下文法模型的小波阈值去噪算法[J]. 计算机应用, 2005, 25(05): 1096-1098.
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URL: http://www.joca.cn/EN/10.3724/SP.J.1087.2005.1096
http://www.joca.cn/EN/Y2005/V25/I05/1096