Bayesian sparse image reconstruction for MRFM - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2009

Bayesian sparse image reconstruction for MRFM

Résumé

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.

Dates et versions

hal-04248403 , version 1 (18-10-2023)

Identifiants

Citer

Nicolas Dobigeon, Alfred Hero, Jean-Yves Tourneret. Bayesian sparse image reconstruction for MRFM. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2009), IEEE, Apr 2009, Taipei, ROC, Taiwan. pp.2933--2936, ⟨10.1109/ICASSP.2009.4960238⟩. ⟨hal-04248403⟩
17 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More