Sparse phase retrieval of one-dimensional signals by Prony's method
Provides a theoretical guarantee and algorithm for phase retrieval of sparse one-dimensional signals, relevant to signal processing and imaging.
The paper shows that sparse signals (sum of N spikes or B-splines with arbitrary knots) can be almost surely recovered from O(N^2) Fourier intensity measurements, using Prony's method to recover autocorrelation parameters and then the signal parameters. Numerical examples illustrate the algorithm.
In this paper, we show that sparse signals f representable as a linear combination of a finite number N of spikes at arbitrary real locations or as a finite linear combination of B-splines of order m with arbitrary real knots can be almost surely recovered from O(N^2) Fourier intensity measurements up to trivial ambiguities. The constructive proof consists of two steps, where in the first step the Prony method is applied to recover all parameters of the autocorrelation function and in the second step the parameters of f are derived. Moreover, we present an algorithm to evaluate f from its Fourier intensities and illustrate it at different numerical examples.