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Semi-blind sparse image reconstruction with application to MRFM

Park, Se Un and Dobigeon, Nicolas and Hero, Alfred O. Semi-blind sparse image reconstruction with application to MRFM. (2012) IEEE Transactions on Image Processing, vol. 21 (n° 9). pp. 3838-3849. ISSN 1057-7149

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Official URL: http://dx.doi.org/10.1109/TIP.2012.2199505

Abstract

We propose a solution to the image deconvolution problem where the convolution kernel 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 PSF uncertainty in a high-dimensional space. Unlike recent developments on blind deconvolution of natural images, we assume the image is sparse in the pixel basis, a natural sparsity arising in magnetic resonance force microscopy (MRFM). Our approach adopts a Bayesian Metropolis-within-Gibbs sampling framework. The performance of our Bayesian semi-blind algorithm for sparse images is superior to previously proposed semi-blind algorithms such as the alternating minimization algorithm and blind algorithms developed for natural images. We illustrate our myopic algorithm on real MRFM tobacco virus data.

Item Type:Article
Additional Information:Thanks to IEEE editor. The definitive version is available at http://dx.doi.org/10.1109/TIP.2012.2199505
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS
Université de Toulouse > Institut National Polytechnique de Toulouse - INPT
Université de Toulouse > Université Paul Sabatier-Toulouse III - UPS
Université de Toulouse > Université de Toulouse II-Le Mirail - UTM
Université de Toulouse > Université de Toulouse I-Sciences Sociales - UT1
Other partners > University of Michigan - U-M (USA)
Laboratory name:
Statistics:download
Deposited By: Nicolas DOBIGEON

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