Edge preserving tomographic reconstruction from few projections

The problem of image reconstruction from a small number of projections is ill-posed and the standard techniques are generally unable to compute suitable solutions. In this paper a regularization approach is proposed which exploits pre-existing knowledge on the image discontinuities. An image model based on a pair of Markov Random Fields, associated with the intensity process and with the line process, respectively, is introduced. The Gibbs distribution is used to define the prior densities. The final solution is thus computed as a Maximum A Posteriori estimate, via a mixed stochastic-deterministic algorithm. Some results from computer aided simulations are presented.