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Global Optimal Image Reconstruction from Blurred Noisy Data by a Bayesian Approach
Authors:Bruni  C.  Bruni  R.  De Santis  A.  Iacoviello  D.  Koch  G.
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
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.
Keywords:Image analysis  global constrained optimization  Bayesian modeling  wavelet processing
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