%0 Conference Proceedings
%T Phase Retrieval with a Multivariate Von Mises Prior: From a Bayesian Formulation to a Lifting Solution
%+ Lab-STICC_ENSTAB_CID_TOMS
%+ Pôle STIC_AP
%+ Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio (PANAMA)
%A Drémeau, Angélique
%A Deleforge, Antoine
%< avec comité de lecture
%B ICASSP 2017 - 42nd IEEE International Conference on Acoustics, Speech and Signal Processing
%C New Orleans, United States
%P 1-5
%8 2017-03-05
%D 2017
%R 10.1109/ICASSP.2017.7953027
%K lifting
%K Mahalanobis distance
%K Phase retrieval
%K multivariate Von Mises distribution
%Z Statistics [stat]/Methodology [stat.ME]Conference papers
%X 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.
%G English
%2 https://hal.science/hal-01653732/document
%2 https://hal.science/hal-01653732/file/07953027.pdf
%L hal-01653732
%U https://hal.science/hal-01653732
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