Title :
ML parameter estimation for Markov random fields with applications to Bayesian tomography
Author :
Saquib, Suhail S. ; Bouman, Charles A. ; Saue, Ken
Author_Institution :
Polaroid Corp., Cambridge, MA, USA
fDate :
7/1/1998 12:00:00 AM
Abstract :
Markov random fields (MRFs) 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: (i) direct parameter estimation for MRFs is known to be mathematically and numerically challenging; (ii) parameters can not be directly estimated because the true image cross section is unavailable. 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 generalized Gaussian MRF (GGMRF). We derive 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. We present a fast algorithm for computing ML parameter estimates when the true image is unavailable. To do this, we use the expectation maximization (EM) algorithm. We develop a fast simulation method to replace the E-step, and a method to improve the parameter estimates when the simulations are terminated prior to convergence. Experimental results indicate that our fast algorithms substantially reduce the computation and result in good scale estimates for real tomographic data sets
Keywords :
Bayes methods; Gaussian processes; Markov processes; computerised tomography; convergence of numerical methods; image reconstruction; maximum likelihood estimation; medical image processing; random processes; Bayesian tomography; EM algorithm; GGMRF; ML parameter estimation; MRF models; Markov random fields; closed-form solution; computationally efficient scheme; expectation maximization algorithm; experimental results; fast simulation method; generalized Gaussian MRF; hyperparameters; image cross section; image modelling; image reconstruction; image restoration; optimal estimation; partition function; scale parameters; shape parameters; tomographic data sets; Bayesian methods; Closed-form solution; Computational modeling; Image reconstruction; Image restoration; Markov random fields; Maximum likelihood estimation; Parameter estimation; Partitioning algorithms; Shape;
Journal_Title :
Image Processing, IEEE Transactions on