Babacan S Derin, Molina Rafael, Katsaggelos Aggelos K
Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208-3118, USA.
IEEE Trans Image Process. 2008 Mar;17(3):326-39. doi: 10.1109/TIP.2007.916051.
In this paper, we propose novel algorithms for total variation (TV) based image restoration and parameter estimation utilizing variational distribution approximations. Within the hierarchical Bayesian formulation, the reconstructed image and the unknown hyper parameters for the image prior and the noise are simultaneously estimated. The proposed algorithms provide approximations to the posterior distributions of the latent variables using variational methods. We show that some of the current approaches to TV-based image restoration are special cases of our framework. Experimental results show that the proposed approaches provide competitive performance without any assumptions about unknown hyper parameters and clearly outperform existing methods when additional information is included.
在本文中，我们提出了基于全变差（TV）的图像恢复和参数估计的新算法，该算法利用变分分布近似。在分层贝叶斯公式中，同时估计重建图像以及图像先验和噪声的未知超参数。所提出的算法使用变分方法对潜在变量的后验分布进行近似。我们表明，当前一些基于TV的图像恢复方法是我们框架的特殊情况。实验结果表明，所提出的方法在不对未知超参数做任何假设的情况下具有竞争力，并且当包含额外信息时明显优于现有方法。
