%0 Journal Article %1 Jalobeanu2002341 %A Jalobeanu, André %A Blanc-Féraud, Laure %A Zerubia, Josiane %D 2002 %J Pattern Recognition %K bayesian imageprocessing inference inversions markovrandomfields models regularization remotesensing statistics uncertainty %N 2 %P 341--352 %R DOI: 10.1016/S0031-3203(00)00178-3 %T Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method %U http://www.sciencedirect.com/science/article/B6V14-44HT45G-4/2/ebb6155d9b582e44ff8944d34283d91f %V 35 %X The satellite image deconvolution problem is ill-posed and must be regularized. Herein, we use an edge-preserving regularization model using a phi function, involving two hyperparameters. Our goal is to estimate the optimal parameters in order to automatically reconstruct images. We propose to use the maximum-likelihood estimator (MLE), applied to the observed image. We need sampling from prior and posterior distributions. Since the convolution prevents use of standard samplers, we have developed a modified Geman-Yang algorithm, using an auxiliary variable and a cosine transform. We present a Markov chain Monte Carlo maximum-likelihood (MCMCML) technique which is able to simultaneously achieve the estimation and the reconstruction.

@article{Jalobeanu2002341, abstract = {The satellite image deconvolution problem is ill-posed and must be regularized. Herein, we use an edge-preserving regularization model using a [phi] function, involving two hyperparameters. Our goal is to estimate the optimal parameters in order to automatically reconstruct images. We propose to use the maximum-likelihood estimator (MLE), applied to the observed image. We need sampling from prior and posterior distributions. Since the convolution prevents use of standard samplers, we have developed a modified Geman-Yang algorithm, using an auxiliary variable and a cosine transform. We present a Markov chain Monte Carlo maximum-likelihood (MCMCML) technique which is able to simultaneously achieve the estimation and the reconstruction.}, added-at = {2009-08-24T16:22:14.000+0200}, author = {Jalobeanu, André and Blanc-Féraud, Laure and Zerubia, Josiane}, biburl = {https://www.bibsonomy.org/bibtex/2f9d4cc271ecc61799e0308065fbacc16/jgomezdans}, description = {ScienceDirect - Pattern Recognition : Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method}, doi = {DOI: 10.1016/S0031-3203(00)00178-3}, interhash = {a09fc2d1823691949177ee71e18ad9e7}, intrahash = {f9d4cc271ecc61799e0308065fbacc16}, issn = {0031-3203}, journal = {Pattern Recognition}, keywords = {bayesian imageprocessing inference inversions markovrandomfields models regularization remotesensing statistics uncertainty}, number = 2, pages = {341--352}, timestamp = {2009-08-24T16:22:14.000+0200}, title = {Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method}, url = {http://www.sciencedirect.com/science/article/B6V14-44HT45G-4/2/ebb6155d9b582e44ff8944d34283d91f}, volume = 35, year = 2002 }