7 February 2011 Myopic sparse image reconstruction with application to MRFM
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Proceedings Volume 7873, Computational Imaging IX; 787303 (2011) https://doi.org/10.1117/12.881450
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States
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.
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Se Un Park, Se Un Park, Nicolas Dobigeon, Nicolas Dobigeon, Alfred O. Hero, Alfred O. Hero, } "Myopic sparse image reconstruction with application to MRFM", Proc. SPIE 7873, Computational Imaging IX, 787303 (7 February 2011); doi: 10.1117/12.881450; https://doi.org/10.1117/12.881450
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