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ICIP
2006
IEEE

Sparse Image Reconstruction using Sparse Priors

14 years 5 months ago
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).
Michael Ting, Raviv Raich, Alfred O. Hero
Added 22 Oct 2009
Updated 22 Oct 2009
Type Conference
Year 2006
Where ICIP
Authors Michael Ting, Raviv Raich, Alfred O. Hero
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