| 小波域联合概率分布模型与Bayesian图像去噪 |
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| 引用本文： | 谢志宏,沈庭芝,韩月秋,朱亚平.小波域联合概率分布模型与Bayesian图像去噪[J].北京理工大学学报,2005,25(4):356-359. |
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| 作者姓名： | 谢志宏 沈庭芝 韩月秋 朱亚平 |
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| 作者单位： | 北京理工大学,信息科学技术学院电子工程系,北京,100081;北京理工大学,信息科学技术学院电子工程系,北京,100081;北京理工大学,信息科学技术学院电子工程系,北京,100081;北京理工大学,信息科学技术学院电子工程系,北京,100081 |
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| 摘 要： | 基于小波分解的图像小波系数在层内和层间解相关而相互依存的客观现实,提出了一个联合层内和层间两方向系数的非高斯联合概率分布模型.以此模型作为先验分布,在Bayesian估计理论的框架下,导出小波系数闭式的最大后验(MAP)估计公式,并用高斯噪声污染的典型图像进行了实验.结果显示,由该估计公式计算得到的去噪图像不仅有较少的均方误差(MSE),还具有保护和增强边缘的能力.
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| 关 键 词： | 图像去噪 小波系数模型 概率分布 |
| 文章编号： | 1001-0645(2005)04-0356-04 |
| 收稿时间： | 2004/6/16 0:00:00 |
| 修稿时间： | 2004年6月16日 |
Bayesian Image Denoising Based on Joint Probability Distribution Model in Wavelet Domain |
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| XIE Zhi-hong,SHEN Ting-zhi,HAN Yue-qiu and ZHU Ya-ping.Bayesian Image Denoising Based on Joint Probability Distribution Model in Wavelet Domain[J].Journal of Beijing Institute of Technology(Natural Science Edition),2005,25(4):356-359. |
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| Authors: | XIE Zhi-hong SHEN Ting-zhi HAN Yue-qiu and ZHU Ya-ping |
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| Institution: | Department of Electronic Engineering, School of Information Science and Technology, Beijing Institute of Technology, Beijing100081, China;Department of Electronic Engineering, School of Information Science and Technology, Beijing Institute of Technology, Beijing100081, China;Department of Electronic Engineering, School of Information Science and Technology, Beijing Institute of Technology, Beijing100081, China;Department of Electronic Engineering, School of Information Science and Technology, Beijing Institute of Technology, Beijing100081, China |
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| Abstract: | Based on the inter-and intra-scale coefficients' decorrelating but also the dependent properties of wavelet-based decomposed image, a new local non-Gaussian joint probability distribution model is proposed, and following that, a new closed maximum a posteriori(MAP) estimating formula is derived under the Bayesian estimation theory by using this model as the prior distribution model. At last, several numerical examples are given, the experiments show the denoised images have not only a lower mean-square error(MSE) ,but also a better ability of edge preservation and enhancement. |
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| Keywords: | image denoising wavelet coefficients model probability distribution |
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