Sparse Image Reconstruction using Sparse Priors

Sparse image reconstruction is of interest in the fields of radioastronomy and molecular imaging. The observation is assumed to be a linear transformation of the image, and corrupted by additive white Gaussian noise. We study the usage of sparse priors in the empirical Bayes framework: it permits the selection of the hyperparameters of the prior in a data-driven fashion. Three sparse image reconstruction methods are proposed. A simulation study was performed using a binary-valued image and a Gaussian point spread function. In the range of signal to noise ratios considered, the proposed methods had better performance than sparse Bayesian learning (SBL).

• Type: Conference
• Year: 2006
• Where: ICIP
• Authors: Michael Ting, Raviv Raich, Alfred O. Hero
• Keywords: ICIP 2006, Image Processing, Image Reconstruction Methods, Sparse Bayesian Learning, Sparse Image Reconstruction, Sparse Priors, White Gaussian Noise