Stable phase retrieval from short-time linear canonical transforms of signals in Gaussian shift-invariant spaces

TL;DR

This paper addresses phase retrieval from short-time linear canonical transform measurements in Gaussian shift-invariant spaces, providing explicit reconstruction formulas, stability analysis, and a robust algorithm with quantitative guarantees.

Contribution

It introduces the first explicit reconstruction formula and stability analysis for phase retrieval from phaseless STLCT measurements in Gaussian shift-invariant spaces, along with a robust reconstruction algorithm.

Findings

Unique determination of signals up to a global phase from phaseless measurements.

Stability constant depends on maximal spacing between anchor points, not interval radius.

Reconstruction algorithm with quantitative robustness guarantees in noisy, finite-data settings.

Abstract

Gabor phase retrieval for signals has attracted considerable attention in recent years. For the more general short-time linear canonical transform (STLCT), which arises naturally in optical systems and canonical time--frequency analysis, existing work has so far focused mainly on uniqueness and sampling conditions. Explicit reconstruction formulas, quantitative stability estimates, and robust reconstruction algorithms, however, are still missing. In this paper, we study uniqueness, stability, and robust reconstruction for phase retrieval from phaseless STLCT measurements in the complex Gaussian shift-invariant space $V_\beta^\infty(\varphi)$. We first prove that every signal in $V_\beta^\infty(\varphi)$ is uniquely determined, up to a global unimodular constant, by its phaseless STLCT measurements on the semi-discrete set $\frac{\beta}{2}\mathbb Z\times\mathbb R$, and we derive an explicit reconstruction formula. We then establish stability on intervals under an anchor-point condition, showing that the stability constant is governed by the maximal spacing between adjacent anchor points rather than by the radius of the whole interval. This prevents exponential deterioration with respect to the interval size. Motivated by the practical setting in which only finitely many discrete noisy magnitude samples are available, we further develop an explicit reconstruction algorithm with quantitative robustness guarantees, where the reconstruction error is controlled by the discretization parameters, the noise level, and the conditioning induced by the anchor points. In the Fourier case, our results recover the corresponding Gabor phase retrieval results of Grohs and Liehr and provide improved stability constants.