Phase retrieval with a multivariate Von Mises prior: from a Bayesian formulation to a lifting solution
Information Theory
2017-05-01 v1 math.IT
Abstract
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.
Cite
@article{arxiv.1704.08972,
title = {Phase retrieval with a multivariate Von Mises prior: from a Bayesian formulation to a lifting solution},
author = {Angelique Dremeau and Antoine Deleforge},
journal= {arXiv preprint arXiv:1704.08972},
year = {2017}
}
Comments
Preprint of the paper published in the proc. of ICASSP'17