Chantas Giannis K, Galatsanos Nikolaos P, Likas Aristidis C
Department of Computer Science, University of Ioannina, Greece.
IEEE Trans Image Process. 2006 Oct;15(10):2987-97. doi: 10.1109/tip.2006.877520.
In this paper, we propose a class of image restoration algorithms based on the Bayesian approach and a new hierarchical spatially adaptive image prior. The proposed prior has the following two desirable features. First, it models the local image discontinuities in different directions with a model which is continuous valued. Thus, it preserves edges and generalizes the on/off (binary) line process idea used in previous image priors within the context of Markov random fields (MRFs). Second, it is Gaussian in nature and provides estimates that are easy to compute. Using this new hierarchical prior, two restoration algorithms are derived. The first is based on the maximum a posteriori principle and the second on the Bayesian methodology. Numerical experiments are presented that compare the proposed algorithms among themselves and with previous stationary and non stationary MRF-based with line process algorithms. These experiments demonstrate the advantages of the proposed prior.
在本文中，我们提出了一类基于贝叶斯方法和一种新的分层空间自适应图像先验的图像恢复算法。所提出的先验具有以下两个理想特性。首先，它用一个连续值模型对不同方向上的局部图像不连续性进行建模。因此，它保留了边缘，并在马尔可夫随机场（MRF）的背景下推广了先前图像先验中使用的开/关（二进制）线过程思想。其次，它本质上是高斯的，并且提供易于计算的估计。使用这种新的分层先验，推导了两种恢复算法。第一种基于最大后验概率原理，第二种基于贝叶斯方法。给出了数值实验，将所提出的算法相互比较，并与先前基于平稳和非平稳MRF且带有线过程的算法进行比较。这些实验证明了所提出先验的优势。
