10 February 2012 Variational semi-blind sparse image reconstruction with application to MRFM
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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).
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Se Un Park, Se Un Park, Nicolas Dobigeon, Nicolas Dobigeon, Alfred O. Hero, Alfred O. Hero, } "Variational semi-blind sparse image reconstruction with application to MRFM", Proc. SPIE 8296, Computational Imaging X, 82960G (10 February 2012); doi: 10.1117/12.923764; https://doi.org/10.1117/12.923764

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