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ICASSP
2009
IEEE

Bayesian sparse image reconstruction for MRFM

10 years 4 months ago
Bayesian sparse image reconstruction for MRFM
In this paper, we propose a Bayesian model and a Monte Carlo Markov chain (MCMC) algorithm for reconstructing images that consist of only few non-zero pixels. An appropriate distribution that promotes sparsity is proposed as prior distribution for the pixel values. The hyperparameters involved in the modeling are also assigned prior distributions, resulting in a hierarchical model. A Gibbs sampler allows us to draw samples distributed according the full posterior of interest. These samples are then used to approximate standard maximum a posteriori (MAP) estimator. By conducting some simulations, we show that the proposed estimator clearly outperforms previous estimators proposed in the literature.
Nicolas Dobigeon, Alfred O. Hero, Jean-Yves Tourne
Added 21 May 2010
Updated 21 May 2010
Type Conference
Year 2009
Where ICASSP
Authors Nicolas Dobigeon, Alfred O. Hero, Jean-Yves Tourneret
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