Efficient Compressive Phase Retrieval with Constrained Sensing Vectors

TL;DR

This paper introduces a robust, two-stage convex optimization approach for compressive phase retrieval that leverages constrained sensing vectors to efficiently recover sparse signals with optimal measurement complexity.

Contribution

It presents a novel convex relaxation framework for jointly sparse and low-rank matrix recovery in phase retrieval using random incoherent sensing vectors.

Findings

Achieves accurate recovery with O(k log(d/k)) measurements.

Decouples low-rank and sparse structures effectively.

Demonstrates robustness through numerical simulations.

Abstract

We propose a robust and efficient approach to the problem of compressive phase retrieval in which the goal is to reconstruct a sparse vector from the magnitude of a number of its linear measurements. The proposed framework relies on constrained sensing vectors and a two-stage reconstruction method that consists of two standard convex programs that are solved sequentially. In recent years, various methods are proposed for compressive phase retrieval, but they have suboptimal sample complexity or lack robustness guarantees. The main obstacle has been that there is no straightforward convex relaxations for the type of structure in the target. Given a set of underdetermined measurements, there is a standard framework for recovering a sparse matrix, and a standard framework for recovering a low-rank matrix. However, a general, efficient method for recovering a jointly sparse and low-rank matrix has remained elusive. Deviating from the models with generic measurements, in this paper we show that if the sensing vectors are chosen at random from an incoherent subspace, then the low-rank and sparse structures of the target signal can be effectively decoupled. We show that a recovery algorithm that consists of a low-rank recovery stage followed by a sparse recovery stage will produce an accurate estimate of the target when the number of measurements is $\mathsf{O}(k\,\log\frac{d}{k})$, where $k$ and $d$ denote the sparsity level and the dimension of the input signal. We also evaluate the algorithm through numerical simulation.