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.

• Type: Conference
• Year: 2009
• Where: ICASSP
• Authors: Nicolas Dobigeon, Alfred O. Hero, Jean-Yves Tourneret
• Keywords: ICASSP 2009, Monte Carlo Markov, Non-zero Pixels, Prior Distributions, Signal Processing