IEEE Trans Image Process - Joint NDT image restoration and segmentation using Gauss-Markov-Potts prior models and variational Bayesian computation.

Tópicos

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Resumo

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.

Resumo Limpo

paper propos method simultan restor segment piecewis homogen imag degrad known point spread function psf addit nois purpos propos famili nonhomogen gaussmarkov field pott region label model imag use bayesian estim framework joint posterior law unknown unknown imag segment hidden variabl hyperparamet approxim separ probabl law via variat bay techniqu approxim give possibl obtain practic implement joint restor segment algorithm will present preliminari result comparison mcmc gibb sampl base algorithm may note prior model propos work particular appropri imag scene object compos finit set homogen materi case mani imag obtain nondestruct test ndt applic

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