Abstract: In some applications of tomographic reconstruction of 2D or 3D fields, the presence of physical constraints allows measurement of projections only within limited angular views; in these cases, a satisfactory resolution may be obtained only with the addition of a priori information about the structure to be estimated. Besides deterministic constraints, some form of probabilistic knowledge is often available, which could help to eliminate a large class of unlikely solutions in the inversion process. However, despite their elegant simplicity, some proposed Gaussian models have shown to be inadequate to satisfactory model objects in actual environments. For these reasons, the paper proposes a flexible probabilistic model based on Gibbs Random Field (GRF) which can be used for tomographic reconstruction. The theoretical framework of the method is described and the performance of the algorithm are illustrated through some simulated experiments.!6
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