A non-homogeneous MRF model for multiresolution Bayesian estimation
A non-homogeneous MRF model for multiresolution Bayesian estimation
复制标题
用于多分辨率贝叶斯估计的非齐次 MRF 模型
DOI：
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发表时间：
1996
期刊：
影响因子：
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通讯作者：
K. Sauer
中科院分区：
文献类型：
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作者：
S. S. Saquib;C. Bouman;K. Sauer
贝叶斯方法在图像处理应用中的流行引起了人们对图像建模的极大兴趣。一个好的图像模型需要是非均匀的，以便能够适应图像中不同区域的局部特征。然而在过去，这样的公式很困难，因为不清楚如何选择非齐次模型的参数。但现在受到 MRF 模型最大似然参数估计结果的启发，我们在本文中制定了多分辨率框架中的非齐次马尔可夫随机场 (MRF) 图像模型。多分辨率框架的优点有两个：首先，它可以通过使用较粗分辨率的图像来估计任何分辨率下非均匀 MRF 的参数。其次，它产生的多分辨率算法比单分辨率算法计算效率更高且更稳健。层析图像重建和光流计算问题的实验结果验证了新模型提供的优越建模能力。
The popularity of Bayesian methods in image processing applications has generated great interest in image modeling. A good image model needs to be non-homogeneous to be able to adapt to the local characteristics of the different regions in an image. In the past however, such a formulation was difficult since it was not clear as to how to choose the parameters of the non-homogeneous model. But now motivated by results in maximum likelihood parameter estimation of MRF models, we formulate in this paper a non-homogeneous Markov random field (MRF) image model in the multiresolution framework. The advantage of the multiresolution framework is two fold: first, it makes it possible to estimate the parameters of the nonhomogeneous MRF at any resolution by using the image at the coarser resolution. Second, it yields multiresolution algorithms which are computationally efficient and more robust than their single resolution counterparts. Experimental results in tomographic image reconstruction and optical flow computation problems verify the superior modeling provided by the new model.