Lower Lipschitz Bounds for Phase Retrieval from Locally Supported Measurements

TL;DR

This paper establishes lower bounds on the robustness of phase retrieval algorithms from local measurements, indicating that high-resolution imaging with localized probes requires many measurements for noise stability.

Contribution

It provides fundamental limits on the noise robustness of phase retrieval from local correlation measurements, applicable to various imaging modalities.

Findings

Robustness bounds imply large measurement requirements for high-resolution imaging.

Local measurement schemes face intrinsic stability limitations.

Results are relevant for designing probes and measurement strategies in practice.

Abstract

In this short note, we consider the worst case noise robustness of any phase retrieval algorithm which aims to reconstruct all nonvanishing vectors $\mathbf{x} \in \mathbb{C}^d$ (up to a single global phase multiple) from the magnitudes of an arbitrary collection of local correlation measurements. Examples of such measurements include both spectrogram measurements of $\mathbf{x}$ using locally supported windows and masked Fourier transform intensity measurements of $\mathbf{x}$ using bandlimited masks. As a result, the robustness results considered herein apply to a wide range of both ptychographic and Fourier ptychographic imaging scenarios. In particular, the main results imply that the accurate recovery of high-resolution images of extremely large samples using highly localized probes is likely to require an extremely large number of measurements in order to be robust to worst case measurement noise, independent of the recovery algorithm employed. Furthermore, recent pushes to achieve high-speed and high-resolution ptychographic imaging of integrated circuits for process verification and failure analysis will likely need to carefully balance probe design (e.g., their effective time-frequency support) against the total number of measurements acquired in order for their imaging techniques to be stable to measurement noise, no matter what reconstruction algorithms are applied.