基于高斯比例混合模型的图像非下采样Contourlet域去噪

周汉飞; 王孝通; 徐晓刚

doi:10.3724/SP.J.1146.2008.00588

基于高斯比例混合模型的图像非下采样Contourlet域去噪

doi: 10.3724/SP.J.1146.2008.00588

周汉飞^{①③;王孝通},
王孝通,
徐晓刚

基金项目:

辽宁省自然科学基金(20062191)和浙江大学CADCG国家重点实验室开放基金资助课题

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出版历程
- 收稿日期: 2008-05-14
- 修回日期: 2009-03-26
- 刊出日期: 2009-08-19

Image Denoising Using Gaussian Scale Mixture Model in the Nonsubsampled Contourlet Domain

摘要

摘要: 为改善图像的去噪效果，该文提出了一种基于高斯比例混合模型的图像非下采样Contourlet域去噪算法。该算法首先建立非下采样Contourlet系数邻域的高斯比例混合模型，然后在模型基础上应用贝叶斯最小二乘法对系数进行估计，最后反变换得到恢复图像。算法结合了非下采样Contourlet变换对图像边缘的高效表示能力、非下采样变换的移不变性质以及GSM模型对非下采样Contourlet系数邻域相关性的概括能力。实验结果表明，该算法在视觉效果和峰值信噪比的改善上都取得了非常好的效果。
- 图像去噪;非下采样Contourlet变换;高斯比例混合
Abstract: A new method which using Gaussian scale mixtures model in the nonsubsampled Contourlet domain is proposed for image denoising. First, a Gaussian scale mixture model is introduced in order to capturing the correlation of nonsubsampled Contourlet locally coefficients. Then the coefficients are estimated by Bayes least squares estimator based on the model. Finally, the inverse nonsubsampled Contourlet transform is applied to the modified coefficients. This arithmetic combines the character of nonsubsampled Contourlet for image edge representation, shift-invariance and the effective of Gaussian scale mixture model for capturing correlation of locally coefficients. The numerical experimental results show the validity of the proposed method.

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