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Proceedings Paper

Myopic sparse image reconstruction with application to MRFM
Author(s): Se Un Park; Nicolas Dobigeon; Alfred O. Hero
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Paper Abstract

We propose a solution to the image deconvolution problem where the convolution operator or point spread function (PSF) is assumed to be only partially known. Small perturbations generated from the model are exploited to produce a few principal components explaining the uncertainty in a high dimensional space. Specifically, we assume the image is sparse corresponding to the natural sparsity of magnetic resonance force microscopy (MRFM). Our approach adopts a Bayesian Metropolis-within-Gibbs sampling framework. The performance of our Bayesian myopic algorithm is superior to previously proposed algorithms such as the alternating minimization (AM) algorithm for sparse images. We illustrate our myopic algorithm on real MRFM tobacco virus data.

Paper Details

Date Published: 7 February 2011
PDF: 14 pages
Proc. SPIE 7873, Computational Imaging IX, 787303 (7 February 2011); doi: 10.1117/12.881450
Show Author Affiliations
Se Un Park, Univ. of Michigan (United States)
Nicolas Dobigeon, Univ. of Toulouse (France)
Alfred O. Hero, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 7873:
Computational Imaging IX
Charles A. Bouman; Ilya Pollak; Patrick J. Wolfe, Editor(s)

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