Phase Retrieval from 1D Fourier Measurements: Convexity, Uniqueness, and Algorithms

Abstract

This paper considers phase retrieval from the magnitude of 1D over-sampled Fourier measurements, a classical problem that has challenged researchers in various fields of science and engineering. We show that an optimal vector in a least-squares sense can be found by solving a convex problem, thus establishing a hidden convexity in Fourier phase retrieval. We also show that the standard semidefinite relaxation approach yields the optimal cost function value (albeit not necessarily an optimal solution) in this case. A method is then derived to retrieve an optimal minimum phase solution in polynomial time. Using these results, a new measuring technique is proposed which guarantees uniqueness of the solution, along with an efficient algorithm that can solve large-scale Fourier phase retrieval problems with uniqueness and optimality guarantees.

References in corpus (3)

• Sparse Phase Retrieval from Short-Time Fourier Measurements
• Phase Retrieval: An Overview of Recent Developments
• Phase Retrieval Using Feasible Point Pursuit: Algorithms and Cramér-Rao Bound

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