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

  • Fabrice HumblotEmail author
  • Ali Mohammad-Djafari
Open Access
Research Article
First Online:
Part of the following topical collections:
  1. Super-Resolution Imaging: Analysis, Algorithms, and Applications

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.

Keywords

Information Technology Markov Chain Markov Model Mixture Model Hide Markov Model 
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Copyright information

© 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.

Authors and Affiliations

  1. 1.DGA/DET/SCET/CEP/ASC/GIPArcueilFrance
  2. 2.LSS/UMR8506 (CNRS-Supèlec-UPS)Gif-sur-Yvette CedexFrance

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