  In this paper, we investigate a new method for phase recovery when prior
information on the missing phases is available. In particular, we propose to
take into account this information in a generic fashion by means of a
multivariate Von Mises dis- tribution. Building on a Bayesian formulation (a
Maximum A Posteriori estimation), we show that the problem can be expressed
using a Mahalanobis distance and be solved by a lifting optimization procedure.
