2014/10/31 by Angélique Drémeau, Florent Krzakala · 1 citation
Computer Science · Mathematics · #cs.IT #math.IT #math.ST #stat.AP #stat.TH
paper · pdf · doi:10.1109/icassp.2015.7178654
published as Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on Year: 2015 Pages: 3661- 3665 · To appear in the proceedings of IEEE Int'l Conference on Acoustics, Speech and Signal Processing (ICASSP)
arxiv created 2015/02/09 · arxiv updated 2015/08/11
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 good computational cost.