Global optimal image reconstruction from blurred noisy data by a Bayesian approach

Authors:
C. Bruni;R. Bruni;A. De Santis;D. Iacoviello;G. Koch
Affiliations:
Professor, 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 Sapi ...;Postdoctoral Fellow, Dipartimento di Informatica e Sistemistica, Università di Roma La Sapienza, Rome, Italy;Professor, 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 Sapi ...;Researcher, Dipartimento di Informatica e Sistemistica, Università di Roma La Sapienza, Rome, Italy;Professor, Dipartimento di Informatica e Sistemistica, Università di Roma La Sapienza, Rome, Italy
Venue:
Journal of Optimization Theory and Applications
Year:
2002

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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.