Superresolution with compound Markov random fields via the variational EM algorithm
Superresolution with compound Markov random fields via the variational EM algorithm
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DOI：
10.1016/j.neunet.2008.12.005
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发表时间：
2009-09-01
期刊：
影响因子：
7.8
通讯作者：
Ishii, Shin
中科院分区：
文献类型：
--
作者：
Kanemura, Atsunori;Maeda, Shin-ichi;Ishii, Shin
本文研究了重建型超分辨率问题及其伴随的图像配准问题。我们提出了一种贝叶斯方法，其中先验被建模为复合高斯马尔可夫随机场（MRF），并对未知变量进行边缘化以避免过拟合。与贝叶斯超分辨率的单层高斯MRF模型不同，我们的算法不仅避免了过拟合，而且保留了估计图像的不连续。使用变分EM算法对配准参数进行最大边际似然估计，其中隐变量被边缘化，后验分布由因式试验分布进行变近似。EM算法通过后验计算过程获得高分辨率图像估计。实验表明，基于双层复合模型的贝叶斯方法在定量度量和视觉质量方面都优于单层模型。(C) 2008 Elsevier Ltd版权所有。
This study deals with a reconstruction-type superresolution problem and the accompanying image registration problem simultaneously. We propose a Bayesian approach in which the prior is modeled as a compound Gaussian Markov random field (MRF) and marginalization is performed over unknown variables to avoid overfitting. Our algorithm not only avoids overfitting, but also preserves discontinuity in the estimated image, unlike existing single-layer Gaussian MRF models for Bayesian superresolution. Maximum-marginal-likelihood estimation of the registration parameters is carried out using a variational EM algorithm where hidden variables are marginalized out, and the posterior distribution is variationally approximated by a factorized trial distribution. High-resolution image estimates are obtained through the process of posterior computation in the EM algorithm. Experiments show that our Bayesian approach with the two-layer compound model exhibits better performance both in quantitative measures and visual quality than the single-layer model. (C) 2008 Elsevier Ltd. All rights reserved.