Non-convex phase retrieval from STFT measurements

Tamir Bendory, Yonina C. Eldar, Nicolas Boumal

Research output: Contribution to journalArticlepeer-review

64 Scopus citations


The problem of recovering a one-dimensional signal from its Fourier transform magnitude, called Fourier phase retrieval, is ill-posed in most cases. We consider the closely-related problem of recovering a signal from its phaseless short-time Fourier transform (STFT) measurements. This problem arises naturally in several applications, such as ultra-short laser pulse characterization and ptychography. The redundancy offered by the STFT enables unique recovery under mild conditions. We show that in some cases the unique solution can be obtained by the principal eigenvector of a matrix, constructed as the solution of a simple least-squares problem. When these conditions are not met, we suggest using the principal eigenvector of this matrix to initialize non-convex local optimization algorithms and propose two such methods. The first is based on minimizing the empirical risk loss function, while the second maximizes a quadratic function on the manifold of phases. We prove that under appropriate conditions, the proposed initialization is close to the underlying signal. We then analyze the geometry of the empirical risk loss function and show numerically that both gradient algorithms converge to the underlying signal even with small redundancy in the measurements. In addition, the algorithms are robust to noise.

Original languageEnglish (US)
Pages (from-to)467-484
Number of pages18
JournalIEEE Transactions on Information Theory
Issue number1
StatePublished - Jan 2018

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences


  • Least-squares
  • Non-convex optimization
  • Optimization on manifolds
  • Phase retrieval
  • Ptychography
  • Short-time Fourier transform
  • Spectral initialization
  • Ultra-short laser pulse characterization


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