Phase Retrieval via Polarization in Dynamical Sampling

TL;DR

This paper addresses the phase retrieval problem in dynamical sampling, proposing conditions and polarization-based methods for stable signal recovery from space-time measurements, with guarantees under certain matrix spectral conditions.

Contribution

It introduces necessary spectral conditions for dynamical sampling systems and develops a polarization technique for stable phase retrieval, extending to full recovery under spark conditions.

Findings

Necessary spectral conditions for sampling vectors are established.

A polarization-based recovery procedure is proposed for almost all signals.

Full spark conditions enable complete signal recovery without exceptions.

Abstract

In this paper we consider the nonlinear inverse problem of phase retrieval in the context of dynamical sampling. Where phase retrieval deals with the recovery of signals & images from phaseless measurements, dynamical sampling was introduced by Aldroubi et al in 2015 as a tool to recover diffusion fields from spatiotemporal samples. Considering finite-dimensional signals evolving in time under the action of a known matrix, our aim is to recover the signal up to global phase in a stable way from the absolute value of certain space-time measurements. First, we state necessary conditions for the dynamical system of sampling vectors to make the recovery of the unknown signal possible. The conditions deal with the spectrum of the given matrix and the initial sampling vector. Then, assuming that we have access to a specific set of further measurements related to aligned sampling vectors, we provide a feasible procedure to recover almost every signal up to global phase using polarization techniques. Moreover, we show that by adding extra conditions like full spark, the recovery of all signals is possible without exceptions.