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Communication Dans Un Congrès Année : 2017

Phase Retrieval with a Multivariate Von Mises Prior: From a Bayesian Formulation to a Lifting Solution

Résumé

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.
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Dates et versions

hal-01653732 , version 1 (05-12-2017)

Identifiants

Citer

Angélique Drémeau, Antoine Deleforge. Phase Retrieval with a Multivariate Von Mises Prior: From a Bayesian Formulation to a Lifting Solution. ICASSP 2017 - 42nd IEEE International Conference on Acoustics, Speech and Signal Processing, Mar 2017, New Orleans, United States. pp.1-5, ⟨10.1109/ICASSP.2017.7953027⟩. ⟨hal-01653732⟩
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