Inexact alternating optimization for phase retrieval with outliers

Cheng Qian, Xiao Fu, Nicholas D. Sidiropoulos, Lei Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Most of the available phase retrieval algorithms were explicitly or implicitly developed under a Gaussian noise model, using least squares (LS) formulations. However, in some applications of phase retrieval, an unknown subset of the measurements can be seriously corrupted by outliers, where LS is not robust and will degrade the estimation performance severely. This paper presents an Alternating Iterative Reweighted Least Squares (AIRLS) method for phase retrieval in the presence of such outliers. The AIRLS employs two-block alternating optimization to retrieve the signal through solving an ℓp-norm minimization problem, where 0 < p < 2. The Cramér-Rao bound (CRB) for Laplacian as well as Gaussian noise is derived for the measurement model considered, and simulations show that the proposed approach outperforms state-of-the-art algorithms in heavy-tailed noise.

Original languageEnglish (US)
Title of host publication2016 24th European Signal Processing Conference, EUSIPCO 2016
PublisherEuropean Signal Processing Conference, EUSIPCO
Number of pages5
ISBN (Electronic)9780992862657
StatePublished - Nov 28 2016
Event24th European Signal Processing Conference, EUSIPCO 2016 - Budapest, Hungary
Duration: Aug 28 2016Sep 2 2016

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Other24th European Signal Processing Conference, EUSIPCO 2016

Bibliographical note

Publisher Copyright:
© 2016 IEEE.


  • Cramér-Rao bound (CRB)
  • Iterative Reweighted Least Squares
  • Phase retrieval


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