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Technical Program
Paper Detail
| Paper: | TA-L6.2 |
| Session: | Signal/Image Reconstruction from Sparse Measurements |
| Time: | Tuesday, October 10, 10:00 - 10:20 |
| Presentation: |
Special Session Lecture
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| Title: |
SPARSE IMAGE RECONSTRUCTION USING SPARSE PRIORS |
| 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). |
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