International Journal of Computer Vision
Statistical approaches in quantitative positron emissiontomography
Statistics and Computing
Pattern Recognition Letters
Estimating Optimal Parameters for MRF Stereo from a Single Image Pair
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Generative Model of Terrain for Autonomous Navigation in Vegetation
International Journal of Robotics Research
Multichannel SAR interferometry via classical and Bayesian estimation techniques
EURASIP Journal on Applied Signal Processing
A 2D approach to tomographic image reconstruction using a Hopfield-type neural network
Artificial Intelligence in Medicine
Image magnification based on a blockwise adaptive Markov random field model
Image and Vision Computing
On image reconstruction algorithms for binary electromagnetic geotomography
Theoretical Computer Science
Bayesian Motion Recovery Framework for Myocardial Phase-Contrast Velocity MRI
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Counter-examples for Bayesian MAP restoration
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Bounds on the minimizers of (nonconvex) regularized least-squares
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Fluorescence diffuse optical image reconstruction with a priori information
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
MRF-MBNN: a novel neural network architecture for image processing
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
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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