Variational Bayesian Blind Deconvolution Using a Total Variation Prior
Variational Bayesian Blind Deconvolution Using a Total Variation Prior
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DOI：
10.1109/tip.2008.2007354
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发表时间：
2009
影响因子：
10.6
通讯作者：
S. D. Babacan;R. Molina;A. Katsaggelos
中科院分区：
文献类型：
--
作者：
S. D. Babacan;R. Molina;A. Katsaggelos
在本文中，我们提出了新的算法的全变分（TV）的盲反卷积和参数估计利用变分框架。使用分层贝叶斯模型，未知的图像，模糊，超参数的图像，模糊和噪声先验估计的同时。利用变分推理的方法，使近似的后验分布的未知数，从而提供了一个衡量的不确定性的估计。实验结果表明，所提出的方法提供了更高的恢复性能比非基于TV的方法，没有任何假设的未知超参数。
In this paper, we present novel algorithms for total variation (TV) based blind deconvolution and parameter estimation utilizing a variational framework. Using a hierarchical Bayesian model, the unknown image, blur, and hyperparameters for the image, blur, and noise priors are estimated simultaneously. A variational inference approach is utilized so that approximations of the posterior distributions of the unknowns are obtained, thus providing a measure of the uncertainty of the estimates. Experimental results demonstrate that the proposed approaches provide higher restoration performance than non-TV-based methods without any assumptions about the unknown hyperparameters.