Fundamentals of digital image processing
Fundamentals of digital image processing
Keeping the neural networks simple by minimizing the description length of the weights
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
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
Neural Computation
Dynamic models for nonstationary signal segmentation
Computers and Biomedical Research
An Introduction to Variational Methods for Graphical Models
Machine Learning
Bayesian parameter estimation via variational methods
Statistics and Computing
Variational mixture of Bayesian independent component analyzers
Neural Computation
The Computer Journal
Variational Bayesian blind deconvolution using a total variation prior
IEEE Transactions on Image Processing
Variational Bayesian sparse kernel-based blind image deconvolution with student's-t priors
IEEE Transactions on Image Processing
Variational Bayes for generalized autoregressive models
IEEE Transactions on Signal Processing
Thresholding implied by truncated quadratic regularization
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
Inversion of large-support ill-posed linear operators using a piecewise Gaussian MRF
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Blind Deconvolution Using a Variational Approach to Parameter, Image, and Blur Estimation
IEEE Transactions on Image Processing
Parameter Estimation in TV Image Restoration Using Variational Distribution Approximation
IEEE Transactions on Image Processing
Variational Bayesian Image Restoration Based on a Product of -Distributions Image Prior
IEEE Transactions on Image Processing
Solution of inverse problems in image processing by wavelet expansion
IEEE Transactions on Image Processing
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In this paper, we propose a method to simultaneously restore and to segment piecewise homogeneous images degraded by a known point spread function (PSF) and additive noise. For this purpose, we propose a family of nonhomogeneous Gauss-Markov fields with Potts region labels model for images to be used in a Bayesian estimation framework. The joint posterior law of all the unknowns (the unknown image, its segmentation (hidden variable) and all the hyperparameters) is approximated by a separable probability law via the variational Bayes technique. This approximation gives the possibility to obtain practically implemented joint restoration and segmentation algorithm. We will present some preliminary results and comparison with a MCMC Gibbs sampling based algorithm. We may note that the prior models proposed in this work are particularly appropriate for the images of the scenes or objects that are composed of a finite set of homogeneous materials. This is the case of many images obtained in nondestructive testing (NDT) applications.