Add to ORKG© 2019 IEEE. The support recovery problem consists of determining a sparse subset of variables that is relevant in generating a set of observations. In this paper, we study the support recovery problem in the phase retrieval model consisting of noisy phaseless measurements, which arises in a diverse range of settings such as optical detection, X-ray crystallography, electron microscopy, and coherent diffractive imaging. Our focus is on information-theoretic fundamental limits under an approximate recovery criterion, with Gaussian measurements and a simple discrete model for the sparse non-zero entries. Our bounds provide sharp thresholds with near-matching constant factors in several scaling regimes on the sparsity and signal-to-noise ratio
We show that the optimal Cramér-Rao lower bound on the mean-square error for the estimation of a coh...
This paper considers the noisy sparse phase retrieval problem: recovering a sparse signal x ∈ ℝp fro...
In a variety of fields, in particular those involving imaging and optics, we often measure signals w...
The support recovery problem consists of determining a sparse subset of variables that is relevant i...
The aim of this paper is to build up the theoretical framework for the recovery of sparse signals fr...
Recovering signals from their Fourier transform magnitudes is a classical problem referred to as pha...
Recovering signals from their Fourier transform magnitudes is a classical problem referred to as pha...
Abstract. In this short note we propose a simple two-stage sparse phase retrieval strategy that uses...
The problem of signal recovery from its Fourier transform magnitude is of paramount importance in v...
We study the problem of recovering a t-sparse real vector from m quadratic equations yi=(ai*x)^2 wit...
Abstract—In a variety of fields, in particular those involving imaging and optics, we often measure ...
In many applications, measurements of a signal consist of the magnitudes of linear functionals while...
In phase retrieval, the goal is to recover a complex signal from the magnitude of its linear measure...
The problem of signal recovery from its Fourier transform magnitude, or equivalently, autocor-relati...
Phase retrieval has been a longstanding problem in optics and x-ray crystallography since the 1970s ...
We show that the optimal Cramér-Rao lower bound on the mean-square error for the estimation of a coh...
This paper considers the noisy sparse phase retrieval problem: recovering a sparse signal x ∈ ℝp fro...
In a variety of fields, in particular those involving imaging and optics, we often measure signals w...
The support recovery problem consists of determining a sparse subset of variables that is relevant i...
The aim of this paper is to build up the theoretical framework for the recovery of sparse signals fr...
Recovering signals from their Fourier transform magnitudes is a classical problem referred to as pha...
Recovering signals from their Fourier transform magnitudes is a classical problem referred to as pha...
Abstract. In this short note we propose a simple two-stage sparse phase retrieval strategy that uses...
The problem of signal recovery from its Fourier transform magnitude is of paramount importance in v...
We study the problem of recovering a t-sparse real vector from m quadratic equations yi=(ai*x)^2 wit...
Abstract—In a variety of fields, in particular those involving imaging and optics, we often measure ...
In many applications, measurements of a signal consist of the magnitudes of linear functionals while...
In phase retrieval, the goal is to recover a complex signal from the magnitude of its linear measure...
The problem of signal recovery from its Fourier transform magnitude, or equivalently, autocor-relati...
Phase retrieval has been a longstanding problem in optics and x-ray crystallography since the 1970s ...
We show that the optimal Cramér-Rao lower bound on the mean-square error for the estimation of a coh...
This paper considers the noisy sparse phase retrieval problem: recovering a sparse signal x ∈ ℝp fro...
In a variety of fields, in particular those involving imaging and optics, we often measure signals w...