## ML parameter estimation for Markov random fields, with applications to Bayesian tomography (1998)

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Venue: | IEEE Trans. on Image Processing |

Citations: | 49 - 18 self |

### BibTeX

@ARTICLE{Saquib98mlparameter,

author = {Suhail S. Saquib and Charles A. Bouman and Ken Sauer},

title = {ML parameter estimation for Markov random fields, with applications to Bayesian tomography},

journal = {IEEE Trans. on Image Processing},

year = {1998},

volume = {7},

pages = {1029--1044}

}

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### Abstract

Abstract 1 Markov random fields (MRF) have been widely used to model images in Bayesian frameworks for image reconstruction and restoration. Typically, these MRF models have parameters that allow the prior model to be adjusted for best performance. However, optimal estimation of these parameters (sometimes referred to as hyperparameters) is difficult in practice for two reasons: 1) Direct parameter estimation for MRF’s is known to be mathematically and numerically challenging. 2) Parameters can not be directly estimated because the true image cross-section is unavailable. In this paper, we propose a computationally efficient scheme to address both these difficulties for a general class of MRF models, and we derive specific methods of parameter estimation for the MRF model known as a generalized Gaussian MRF (GGMRF). The first section of the paper derives methods of direct estimation of scale and shape parameters for a general continuously valued MRF. For the GGMRF case, we show that the ML estimate of the scale parameter, σ, has a simple closed form solution, and we present an efficient scheme for computing the ML estimate of the shape parameter, p, by an off-line numerical computation of the dependence of the partition function on p.