  In this paper, first a great number of inverse problems which arise in
instrumentation, in computer imaging systems and in computer vision are
presented. Then a common general forward modeling for them is given and the
corresponding inversion problem is presented. Then, after showing the
inadequacy of the classical analytical and least square methods for these ill
posed inverse problems, a Bayesian estimation framework is presented which can
handle, in a coherent way, all these problems. One of the main steps, in
Bayesian inversion framework is the prior modeling of the unknowns. For this
reason, a great number of such models and in particular the compound hidden
Markov models are presented. Then, the main computational tools of the Bayesian
estimation are briefly presented. Finally, some particular cases are studied in
detail and new results are presented.
