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Super-Resolution Using Hidden Markov Model and Bayesian Detection Estimation Framework

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This paper presents a new method for super-resolution (SR) reconstruction of a high-resolution (HR) image from several low-resolution (LR) images. The HR image is assumed to be composed of homogeneous regions. Thus, the a priori distribution of the pixels is modeled by a finite mixture model (FMM) and a Potts Markov model (PMM) for the labels. The whole a priori model is then a hierarchical Markov model. The LR images are assumed to be obtained from the HR image by lowpass filtering, arbitrarily translation, decimation, and finally corruption by a random noise. The problem is then put in a Bayesian detection and estimation framework, and appropriate algorithms are developed based on Markov chain Monte Carlo (MCMC) Gibbs sampling. At the end, we have not only an estimate of the HR image but also an estimate of the classification labels which leads to a segmentation result.


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Correspondence to Fabrice Humblot.

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Humblot, F., Mohammad-Djafari, A. Super-Resolution Using Hidden Markov Model and Bayesian Detection Estimation Framework. EURASIP J. Adv. Signal Process. 2006, 036971 (2006) doi:10.1155/ASP/2006/36971

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