Phase Retrieval for Sparse Signals: Uniqueness Conditions

In a variety of fields, in particular those involving imaging and optics, we often measure signals whose phase is missing or has been irremediably distorted. Phase retrieval attempts the recovery of the phase information of a signal from the magnitude of its Fourier transform to enable the reconstruction of the original signal. A fundamental question then is: "Under which conditions can we uniquely recover the signal of interest from its measured magnitudes?" In this paper, we assume the measured signal to be sparse. This is a natural assumption in many applications, such as X-ray crystallography, speckle imaging and blind channel estimation. In this work, we derive a sufficient condition for the uniqueness of the solution of the phase retrieval (PR) problem for both discrete and continuous domains, and for one and multi-dimensional domains. More precisely, we show that there is a strong connection between PR and the turnpike problem, a classic combinatorial problem. We also prove that the existence of collisions in the autocorrelation function of the signal may preclude the uniqueness of the solution of PR. Then, assuming the absence of collisions, we prove that the solution is almost surely unique on 1-dimensional domains. Finally, we extend this result to multi-dimensional signals by solving a set of 1-dimensional problems. We show that the solution of the multi-dimensional problem is unique when the autocorrelation function has no collisions, significantly improving upon a previously known result.Comment: submitted to IEEE TI

An elementary approach for the phase retrieval problem

If the phase retrieval problem can be solved by a method similar to that of solving a system of linear equations under the context of FFT, the time complexity of computer based phase retrieval algorithm would be reduced. Here I present such a method which is recursive but highly non-linear in nature, based on a close look at the Fourier spectrum of the square of the function norm. In a one dimensional problem it takes $O(N^2)$ steps of calculation to recover the phases of an N component complex vector. This method could work in 1, 2 or even higher dimensional finite Fourier analysis without changes in the behavior of time complexity. For one dimensional problem the performance of an algorithm based on this method is shown, where the limitations are discussed too, especially when subject to random noises which contains significant high frequency components.Comment: 4 pages, 4 figure

Common pulse retrieval algorithm: a fast and universal method to retrieve ultrashort pulses

We present a common pulse retrieval algorithm (COPRA) that can be used for a broad category of ultrashort laser pulse measurement schemes including frequency-resolved optical gating (FROG), interferometric FROG, dispersion scan, time domain ptychography, and pulse shaper assisted techniques such as multiphoton intrapulse interference phase scan (MIIPS). We demonstrate its properties in comprehensive numerical tests and show that it is fast, reliable and accurate in the presence of Gaussian noise. For FROG it outperforms retrieval algorithms based on generalized projections and ptychography. Furthermore, we discuss the pulse retrieval problem as a nonlinear least-squares problem and demonstrate the importance of obtaining a least-squares solution for noisy data. These results improve and extend the possibilities of numerical pulse retrieval. COPRA is faster and provides more accurate results in comparison to existing retrieval algorithms. Furthermore, it enables full pulse retrieval from measurements for which no retrieval algorithm was known before, e.g., MIIPS measurements

PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming

Suppose we wish to recover a signal x in C^n from m intensity measurements of the form ||^2, i = 1, 2,..., m; that is, from data in which phase information is missing. We prove that if the vectors z_i are sampled independently and uniformly at random on the unit sphere, then the signal x can be recovered exactly (up to a global phase factor) by solving a convenient semidefinite program---a trace-norm minimization problem; this holds with large probability provided that m is on the order of n log n, and without any assumption about the signal whatsoever. This novel result demonstrates that in some instances, the combinatorial phase retrieval problem can be solved by convex programming techniques. Finally, we also prove that our methodology is robust vis a vis additive noise