Open Access

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

EURASIP Journal on Advances in Signal Processing20062006:036971

https://doi.org/10.1155/ASP/2006/36971

Received: 12 December 2004

Accepted: 27 May 2005

Published: 12 February 2006

Abstract

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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Authors’ Affiliations

(1)
DGA/DET/SCET/CEP/ASC/GIP
(2)
LSS/UMR8506 (CNRS-Supèlec-UPS)

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Copyright

© F. Humblot and A. Mohammad-Djafari. 2006

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.