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

2017/04/28 by Angélique Drémeau, Angelique Dremeau, Dremeau, Angelique +2 · 1 citation
Computer Science · Engineering · Mathematics · Physics and Astronomy · #Advanced X-ray Imaging Techniques #FOS: Computer and information sciences #Hydrocarbon exploration and reservoir analysis #Information Theory (cs.IT) #cs.IT #math.IT

paper · pdf · doi:10.48550/arxiv.1704.08972

Preprint of the paper published in the proc. of ICASSP'17

arxiv created 2017/04/28 · openalex publication_date 2017/04/28 · arxiv updated 2017/05/01 · openalex created_date 2022/10/06 · openalex updated_date 2026/07/28

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 dis- tribution. 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.

Cited by

• Optimal sparse phase retrieval via a quasi-Bayesian approach

Related

• Phase-Retrieval as a Regularization Problem
• Misspecified Phase Retrieval with Generative Priors
• Practical Phase Retrieval Using Double Deep Image Priors
• PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements\n via Convex Programming
• Phase retrieval using alternating minimization in a batch setting