Phase retrieval with a multivariate Von Mises prior: From a Bayesian formulation to a lifting solution

Angélique Dremeau; Antoine Deleforge

ICASSP 2017accepted0 citations

Phase retrieval with a multivariate Von Mises prior: From a Bayesian formulation to a lifting solution

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 distribution. 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.

BibTeX

@inproceedings{icassp2017_phaseretrievalwi,
  title = {Phase retrieval with a multivariate Von Mises prior: From a Bayesian formulation to a lifting solution},
  author = {Angélique Dremeau and Antoine Deleforge},
  booktitle = {ICASSP 2017},
  year = {2017}
}