Inexact Alternating Optimization for Phase Retrieval in the Presence of Outliers

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

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


Phase retrieval has been mainly considered in the presence of Gaussian noise. However, the performance of the algorithms proposed under the Gaussian noise model severely degrades when grossly corrupted data, i.e., outliers, exist. This paper investigates techniques for phase retrieval in the presence of heavy-tailed noise, which is considered a better model for situations where outliers exist. An ℓ lp-norm (0<p<2) based estimator is proposed for fending against such noise, and two-block inexact alternating optimization is proposed as the algorithmic framework to tackle the resulting optimization problem. Two specific algorithms are devised by exploring different local approximations within this framework. Interestingly, the core conditional minimization steps can be interpreted as iteratively reweighted least squares and gradient descent. Convergence properties of the algorithms are discussed, and the Cramér-Rao bound (CRB) is derived. Simulations demonstrate that the proposed algorithms approach the CRB and outperform state-of-the-art algorithms in heavy-tailed noise.

Original languageEnglish (US)
Article number8010463
Pages (from-to)6069-6082
Number of pages14
JournalIEEE Transactions on Signal Processing
Issue number22
StatePublished - Nov 15 2017
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2017 IEEE.


  • Cramér-Rao bound (CRB)
  • Phase retrieval
  • gradient descent
  • impulsive noise
  • iterative reweighted least squares (IRLS)


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