Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Bayesian Approaches to Gaussian Mixture Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic models for nonstationary signal segmentation
Computers and Biomedical Research
Variational mixture of Bayesian independent component analyzers
Neural Computation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Variational Bayes for generalized autoregressive models
IEEE Transactions on Signal Processing
A variational approach for Bayesian blind image deconvolution
IEEE Transactions on Signal Processing
Variational learning for Gaussian mixture models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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In this paper, we propose a method to restore and to segment simultaneously images degraded by a known point spread function (PSF) and additive white noise. For this purpose, we propose a joint Bayesian estimation framework, where a family of nonhomogeneous Gauss-Markov fields with Potts region labels models are chosen to serve as priors for images. Since neither the joint maximum a posteriori estimator nor posterior mean one are tractable, the joint posterior law of the image, its segmentation and all the hyperparameters, is approximated by a separable probability laws using the Variational Bayes technique. This yields a known probability laws of the posterior with mutually dependent shaping parameter, which aims to enhance the convergence speed of the estimator compared to stochastic sampling based estimator. Practical results are presented with comparison to a MCMC based estimator.