Global Optimal Image Reconstruction from Blurred Noisy Data by a Bayesian Approach |
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| Authors: | Bruni C. Bruni R. De Santis A. Iacoviello D. Koch G. |
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| Affiliation: | (1) Dipartimento di Informatica e Sistemistica and Centro Interdipartimentale di Ricerca per l'Analisi dei Modelli e dell'Informazione nei Sistemi Biomedici, Università di Roma La Sapienza, Rome, Italy;(2) Dipartimento di Informatica e Sistemistica, Università di Roma La Sapienza, Rome, Italy;(3) Dipartimento di Informatica e Sistemistica, Università di Roma La Sapienza, Rome, Italy |
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| Abstract: | In this paper, a procedure is presented which allows the optimal reconstruction of images from blurred noisy data. The procedure relies on a general Bayesian approach, which makes proper use of all the available information. Special attention is devoted to the informative content of the edges; thus, a preprocessing phase is included, with the aim of estimating the jump sizes in the gray level. The optimization phase follows; existence and uniqueness of the solution is secured. The procedure is tested against simple simulated data and real data. |
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| Keywords: | Image analysis global constrained optimization Bayesian modeling wavelet processing |
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