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• W2011194005 abstract "In this paper, we propose a Bayesian MAP estimator for solving the deconvolution problems when the observations are corrupted by Poisson noise. Toward this goal, a proper data fidelity term (log-likelihood) is introduced to reflect the Poisson statistics of the noise. On the other hand, as a prior, the images to restore are assumed to be positive and sparsely represented in a dictionary of waveforms such as wavelets or curvelets. Both analysis- and synthesis-type sparsity priors are considered. Piecing together the data fidelity and the prior terms, the deconvolution problem boils down to the minimization of non-smooth convex functionals (for each prior). We establish the well-posedness of each optimization problem, characterize the corresponding minimizers, and solve them by means of proximal splitting algorithms originating from the realm of non-smooth convex optimization theory. Experimental results are conducted to demonstrate the potential applicability of the proposed algorithms to astronomical imaging datasets." @default.
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• W2011194005 date "2012-01-01" @default.
• W2011194005 modified "2023-10-10" @default.
• W2011194005 title "Deconvolution under Poisson noise using exact data fidelity and synthesis or analysis sparsity priors" @default.
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• W2011194005 doi "https://doi.org/10.1016/j.stamet.2011.04.008" @default.
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