基于Bandelets域逐子块阈值的图像去噪  

Image Denoising Using Subsquarewise Threshold in Bandelets Domain

作　　者：张文革[1,2] 刘芳[1,2] 焦李成[2,3] 张向荣[2,3] 

机构地区：[1]西安电子科技大学计算机学院,陕西西安710071 [2]智能感知与图像理解教育部重点实验室,陕西西安710071 [3]西安电子科技大学智能信息处理研究所,陕西西安710071

出　　处：《电子学报》2010年第2期290-294,共5页Acta Electronica Sinica

基　　金：国家自然科学基金(No.60673097;60601029;60972148;60672126;60702062);国家863高技术研究发展计划(No.2008AA01Z125;2007AA12Z136;2008AA12Z223;2009AA12Z210);国家教育部博士点基金(No.2005071007;20070701016);国家部委科技项目(No.XADZ2008159;51307040103)

摘　　要：把正交bandelets域经四叉树分割的每一子块系数建模为广义高斯分布,基于Bayesian框架,推导出了自适应逐子块局部阈值的计算公式,找出了其参数分布的最佳范围,提出了图像bandelets域的逐子块阈值去噪算法.由于充分利用了图像的局部统计信息,所提算法对自然图像去噪的视觉效果和评价指标都好于BayesShrink和其它基于阈值的去噪算法.By modeling orthogonal bandelets coefficients in each quad- tree subsquare as Generalized Gaussian Distribution model, a calculation formula for adaptive local subsquarewise threshold is derived under the Bayesian frame, and the best range of the parameter needed to calculate subsquarewise threshold is found out. On these basis, a subsquarewise threshold denoising algorithm for natural images is proposed in bandelets domain. Owing to making full use of local statistic information of the image, the visual effect and evaluation criteria of proposed algorithm for natural image denoising outperform that of BayesShrink and other threshold-based methods.

关 键 词：图像处理 图像去噪 正交bandelets 逐子块阈值 广义高斯分布 BayesShrink 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]