Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boundary Detection by Constrained Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained Restoration and the Recovery of Discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Modeling by Multiple Pairwise Pixel Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prior Learning and Gibbs Reaction-Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Asymptotic performance analysis of direction-finding algorithmsbased on fourth-order cumulants
IEEE Transactions on Signal Processing
Approximate maximum likelihood hyperparameter estimation for Gibbs priors
IEEE Transactions on Image Processing
ML parameter estimation for Markov random fields with applications to Bayesian tomography
IEEE Transactions on Image Processing
Estimation of Markov random field prior parameters using Markov chain Monte Carlo maximum likelihood
IEEE Transactions on Image Processing
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We investigate hyperparameter estimation for incomplete data in Markov random Field image restoration. Assuming linear dependence of energies with respect to hyperparameters framework, we use a cumulant expansion technique widely known in Statistical Physics and Signal Processing. New insight is given on Maximum Likelihood estimation of hyperparameters of the prior, regularization and contour probability distribution functions (pdfs) for an explicit joint boundary-pixel process aimed to preserve discontinuities. In particular the case where the prior regularization potential is an homogeneous function of pixels is fully analyzed. A Generalized Stochastic Gradient (GSG) algorithm with a fast sampling technique is devised aiming to achieve simultaneous hyperparameter estimation and pixel restoration. Image restoration performances of Posterior Mean performed during GSG convergence and of Simulated Annealing performed after GSG convergence are compared experimentally. Results and perspectives are given.