Super-resolution using Hidden Markov model and Bayesian detection estimation framework

被引：0
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作者：
Humblot, Fabrice [1 ,2 ]
Mohammad-Djafari, Ali [1 ]
机构：
[1] DGA/DET/SCET/CEP/ASC/GIP, Arcueil, 94114, France
[2] LSS/UMR8506 (CNRS-Supèlec-UPS), Gif-sur-Yvette Cedex, 91192, France
来源：
Eurasip Journal on Applied Signal Processing | 2006年 / 2006卷
关键词：
This paper presents a new method for super-resolution (SR)reconstruction of a high-resolution (HR) image from severallow-resolution (LR) images. The HR image is assumed to be composedof homogeneous regions. Thus; the a priori distribution of thepixels is modeled by a finite mixture model (FMM) and a PottsMarkov model (PMM) for the labels. The whole a priori model isthen a hierarchical Markov model. The LR images are assumed to beobtained from the HR image by lowpass filtering; arbitrarilytranslation; decimation; and finally corruption by a random noise.The problem is then put in a Bayesian detection and estimationframework; and appropriate algorithms are developed based onMarkov chain Monte Carlo (MCMC) Gibbs sampling. At the end; wehave not only an estimate of the HR image but also an estimate ofthe classification labels which leads to a segmentation result;
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