Estimating hyperparameters of mixture prior using hypothesis-testing problem and its applications to Bayesian image denoising
Estimating hyperparameters of mixture prior using hypothesis-testing problem and its applications to Bayesian image denoising
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DOI：
10.1117/1.2804153
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发表时间：
2007-10
期刊：
影响因子：
--
通讯作者：
I. Eom;Y. Kim;Do Hoon Lee
中科院分区：
文献类型：
--
作者：
I. Eom;Y. Kim;Do Hoon Lee
我们开发了一种空间自适应贝叶斯图像去噪方法，使用高斯分布和零点质量函数的混合。在估计超参数时，我们提出了一种简单的非迭代方法。我们使用假设检验技术，以估计混合参数，伯努利随机变量。基于估计的混合参数，通过使用最大广义边缘似然（MGML）估计器获得干净信号的方差。我们模拟我们的去噪方法使用正交小波和双树复小波变换，并比较我们的算法，众所周知的去噪方案。实验结果表明，该方法能取得较好的去噪效果。
We develop a spatially adaptive Bayesian image denoising method using a mixture of a Gaussian distribution and a point mass function at zero. In estimating hyperparameters, we present a simple and noniterative method. We use a hypothesis-testing technique in order to estimate the mixing parameter, the Bernoulli random variable. Based on the estimated mixing parameter, the variance for a clean signal is obtained by using the maximum generalized marginal likelihood (MGML) estimator. We simulate our denoising method using both orthogonal wavelet and dual-tree complex wavelet transforms and compare our algorithm to well-known denoising schemes. Experimental results show that the proposed method can generate good denoising results.