Phase recovery from a Bayesian point of view: The variational approach

Angélique Dremeau; Florent Krzakala

ICASSP 2015accepted0 citations

Phase recovery from a Bayesian point of view: The variational approach

Abstract

In this paper, we consider the phase recovery problem, where a complex signal vector has to be estimated from the knowledge of the modulus of its linear projections, from a naive variational Bayesian point of view. In particular, we derive an iterative algorithm following the minimization of the Kullback-Leibler divergence under the mean-field assumption, and show on synthetic data with random projections that this approach leads to an efficient and robust procedure, with a reasonable computational cost.

BibTeX

@inproceedings{icassp2015_phaserecoveryfro,
  title = {Phase recovery from a Bayesian point of view: The variational approach},
  author = {Angélique Dremeau and Florent Krzakala},
  booktitle = {ICASSP 2015},
  year = {2015}
}