Phaseless Sampling and Reconstruction of Real-Valued Signals in Shift-Invariant Spaces

TL;DR

This paper addresses the problem of reconstructing real-valued signals in shift-invariant spaces from magnitude-only measurements, introducing graph-based characterization, finite sampling strategies, and a robust reconstruction algorithm with numerical validation.

Contribution

It introduces a graph connectivity approach for phase retrieval, establishes finite sampling conditions, and proposes a stable reconstruction algorithm for noisy measurements.

Findings

Graph connectivity characterizes signal recoverability.

Finite sampling density enables stable reconstruction.

Numerical simulations demonstrate robustness to noise.

Abstract

Sampling in shift-invariant spaces is a realistic model for signals with smooth spectrum. In this paper, we consider phaseless sampling and reconstruction of real-valued signals in a shift-invariant space from their magnitude measurements on the whole Euclidean space and from their phaseless samples taken on a discrete set with finite sampling density. We introduce an undirected graph to a signal and use connectivity of the graph to characterize whether the signal can be determined, up to a sign, from its magnitude measurements on the whole Euclidean space. Under the local complement property assumption on a shift-invariant space, we find a discrete set with finite sampling density such that signals in the shift-invariant space, that are determined from their magnitude measurements on the whole Euclidean space, can be reconstructed in a stable way from their phaseless samples taken on that discrete set. In this paper, we also propose a reconstruction algorithm which provides a suboptimal approximation to the original signal when its noisy phaseless samples are available only. Finally, numerical simulations are performed to demonstrate the robust reconstruction of box spline signals from their noisy phaseless samples.