ICIP 2006, Atlanta, GA

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Paper Detail

Session:Signal/Image Reconstruction from Sparse Measurements
Time:Tuesday, October 10, 10:00 - 10:20
Presentation: Special Session Lecture
Topic: Special Sessions: Signal/image reconstruction from sparse measurements
Authors: Michael Ting; University of Michigan 
 Raviv Raich; University of Michigan 
 Alfred Hero; University of Michigan 
Abstract: 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).