Almost Everywhere Generalized Phase Retrieval

TL;DR

This paper extends the concept of almost everywhere phase retrieval to generalized quadratic measurements, establishing minimal measurement bounds and covering real, complex, and fusion frame cases with new theoretical insights.

Contribution

It provides new results on the minimal number of measurements needed for almost everywhere phase retrieval in real and complex settings, including special cases like orthogonal matrices and fusion frames.

Findings

For real matrices, N ≥ d+1 generic matrices suffice.

For complex matrices, N ≥ 2d matrices are sufficient.

Existence of N=d (real) and N=2d-1 (complex) matrices with the property.

Abstract

The aim of generalized phase retrieval is to recover $\mathbf{x}\in \mathbb{F}^d$ from the quadratic measurements $\mathbf{x}^*A_1\mathbf{x},\ldots,\mathbf{x}^*A_N\mathbf{x}$, where $A_j\in \mathbf{H}_d(\mathbb{F})$ and $\mathbb{F}=\mathbb{R}$ or $\mathbb{C}$. In this paper, we study the matrix set $\mathcal{A}=(A_j)_{j=1}^N$ which has the almost everywhere phase retrieval property. For the case $\mathbb{F}=\mathbb{R}$, we show that $N\geq d+1$ generic matrices with prescribed ranks have almost everywhere phase retrieval property. We also extend this result to the case where $A_1,\ldots,A_N$ are orthogonal matrices and hence establish the almost everywhere phase retrieval property for the fusion frame phase retrieval. For the case where $\mathbb{F}=\mathbb{C}$, we obtain similar results under the assumption of $N\geq 2d$. We lower the measurement number $d+1$ (resp. $2d$) with showing that there exist $N=d$ (resp. $2d-1$) matrices $A_1,\ldots, A_N\in \mathbf{H}_d(\mathbb{R})$ (resp. $\mathbf{H}_d(\mathbb{C})$) which have the almost everywhere phase retrieval property. Our results are an extension of almost everywhere phase retrieval from the standard phase retrieval to the general setting and the proofs are often based on some new ideas about determinant variety.