Support Recovery for Sparse Multidimensional Phase Retrieval

TL;DR

This paper introduces MISTR, a novel algorithm for support recovery in multidimensional sparse phase retrieval, leveraging autocorrelation and pairwise differences, with proven accuracy and efficiency in noisy and noiseless settings.

Contribution

MISTR is the first multidimensional algorithm that provably recovers sparse signals efficiently from intensity-only measurements, extending one-dimensional turnpike problem solutions.

Findings

MISTR achieves high-probability support recovery for signals with sparsity up to (n^{d heta}) for heta<1/2.

The algorithm performs support recovery with nearly linear time complexity in input size.

In noisy scenarios, a thresholding scheme guarantees support recovery for sparsity up to (n^{d heta}) for heta<1/4.

Abstract

We consider the \textit{phase retrieval} problem of recovering a sparse signal $\mathbf{x}$ in $\mathbb{R}^d$ from intensity-only measurements in dimension $d \geq 2$. Phase retrieval can be equivalently formulated as the problem of recovering a signal from its autocorrelation, which is in turn directly related to the combinatorial problem of recovering a set from its pairwise differences. In one spatial dimension, this problem is well studied and known as the \textit{turnpike problem}. In this work, we present MISTR (Multidimensional Intersection Sparse supporT Recovery), an algorithm which exploits this formulation to recover the support of a multidimensional signal from magnitude-only measurements. MISTR takes advantage of the structure of multiple dimensions to provably achieve the same accuracy as the best one-dimensional algorithms in dramatically less time. We prove theoretically that MISTR correctly recovers the support of signals distributed as a Gaussian point process with high probability as long as sparsity is at most $\mathcal{O}\left(n^{d\theta}\right)$ for any $\theta < 1/2$, where $n^d$ represents pixel size in a fixed image window. In the case that magnitude measurements are corrupted by noise, we provide a thresholding scheme with theoretical guarantees for sparsity at most $\mathcal{O}\left(n^{d\theta}\right)$ for $\theta < 1/4$ that obviates the need for MISTR to explicitly handle noisy autocorrelation data. Detailed and reproducible numerical experiments demonstrate the effectiveness of our algorithm, showing that in practice MISTR enjoys time complexity which is nearly linear in the size of the input.