Variational Bayes with Gauss-Markov-Potts Prior Models for Joint Image Restoration and Segmentation
نویسندگان
چکیده
In this paper, we propose a family of non-homogeneous Gauss-Markov fields with Potts region labels model for images to be used in a Bayesian estimation framework, in order to jointly restore and segment images degraded by a known point spread function and additive noise. 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 laws 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
منابع مشابه
Restoration and Segmentation Using Gauss - Markov - Potts Prior Models and Variational Bayesian Computation : Technical Details
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 non-homogeneous 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 ...
متن کاملA Joint Segmentation and Reconstruction Algorithm for 3d Bayesian Computed Tomography Using Gauss-markov-potts Prior Model
Gauss-Markov-Potts models for images and its use in many image restoration and super-resolution problems have shown their effective use for Non Destructive Testing (NDT) applications. In this paper, we propose a 3D Gauss-Markov-Potts model for 3D CT for NDT applications. Thanks to this model, we are able to perform a joint reconstruction and segmentation of the object to control, which is very ...
متن کاملGauss-Markov-Potts Priors for Images in Computer Tomography Resulting to Joint Optimal Reconstruction and segmentation
In many applications of Computed Tomography (CT), we know that the object under the test is composed of a finite number of materials meaning that the images to be reconstructed are composed of a finite number of homogeneous area. To account for this prior knowledge, we propose a family of Gauss-Markov fields with hidden Potts label fields. Then, using these models in a Bayesian inference framew...
متن کاملA joint segmentation and reconstruction algorithm for 3D Bayesian Computed Tomography using Gaus-Markov-Potts Prior Model
Gauss-Markov-Potts models for images and its use in many image restoration, super-resolution and Computed Tomography (CT) have shown their effective use for Non Destructive Testing (NDT) applications. In this paper, we propose a 3D Gauss-Markov-Potts model for 3D CT for NDT applications. Thanks to this model, we are able to perform a joint reconstruction and segmentation of the object to contro...
متن کاملGauss-Markov-Potts Priors for Images in Computer Tomography Resulting to Joint Reconstruction and segmentation
In many applications of Computed Tomography (CT), we may know that the object under the test is composed of a finite number of materials meaning that the images to be reconstructed are composed of a finite number of homogeneous area. To account for this prior knowledge, we propose a family of Gauss-Markov fields with hidden Potts label fields. Then, using these models in a Bayesian inference fr...
متن کامل