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- W2131570147 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." @default.
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- W2131570147 date "2011-02-10" @default.
- W2131570147 modified "2023-10-03" @default.
- W2131570147 title "Myopic sparse image reconstruction with application to MRFM" @default.
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- W2131570147 doi "https://doi.org/10.1117/12.881450" @default.
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