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Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM

Park, Se Un and Dobigeon, Nicolas and Hero, Alfred O. Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM. (2014) Signal Processing, 94. 386-400. ISSN 0165-1684

(Document in English)

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Official URL: http://dx.doi.org/10.1016/j.sigpro.2013.06.013


We present a variational Bayesian method 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. Importantly, the proposed Bayesian deconvolution algorithm does not require hand tuning. Simulation results clearly demonstrate that the semi-blind deconvolution algorithm compares favorably with previous Markov chain Monte Carlo (MCMC) version of myopic sparse reconstruction. It significantly outperforms mismatched non-blind algorithms that rely on the assumption of the perfect knowledge of the PSF. The algorithm is illustrated on real data from magnetic resonance force microscopy (MRFM).

Item Type:Article
Additional Information:Thanks to Elsevier editor. The definitive version is available at http://www.sciencedirect.com The original PDF of the article can be found at Signal Processing website : http://dx.doi.org/10.1016/j.sigpro.2013.06.013
HAL Id:hal-00875110
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Other partners > University of Michigan - U-M (USA)
Laboratory name:
ARO,Grant number. W911NF-05-1-0403.
Deposited On:30 Aug 2013 09:49

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