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Proceedings Paper

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

This paper addresses the problem of joint image reconstruction and point spread function PSF) estimation when the PSF of the imaging device is only partially known. To solve this semi-blind deconvolution problem, prior distributions are specified for the PSF and the 3D image. Joint image reconstruction and PSF estimation is then performed within a Bayesian framework, using a variational algorithm to estimate the posterior distribution. The image prior distribution imposes an explicit atomic measure that corresponds to image sparsity. Simulation results demonstrate that the semi-blind deconvolution algorithm compares favorably with previous Markov chain Monte Carlo MCMC) version of myopic sparse reconstruction. It also outperforms non-myopic algorithms that rely on perfect knowledge of the PSF. The algorithm is illustrated on real data from magnetic resonance force microscopy MRFM).

Paper Details

Date Published: 10 February 2012
PDF: 11 pages
Proc. SPIE 8296, Computational Imaging X, 82960G (10 February 2012); doi: 10.1117/12.923764
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. 8296:
Computational Imaging X
Charles A. Bouman; Ilya Pollak; Patrick J. Wolfe, Editor(s)

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