Add to ORKGAbstract. This paper is concerned with the question of reconstructing a vector in a finite-dimensional real or complex Hilbert space when only the magnitudes of the coefficients of the vector under a redundant linear map are known. We present new invertibility results as well an iterative algorithm that finds the least-square solution and is robust in the presence of noise. We analyze its numerical performance by comparing it to two versions of the Cramer-Rao lower bound. 1
Phase retrieval in real or complex Hilbert spaces is the task of recovering a vector, up to an overa...
The last decade witnessed the burgeoning development in the reconstruction of signals by exploiting ...
In this paper we consider the classical problem of blind deconvolution of multiple signals from its ...
Abstract. This paper is concerned with the question of reconstructing a vector in a finite-dimension...
In this paper we present a signal reconstruction algorithm from absolute value of frame coefficients...
AbstractWe introduce a simple and efficient method to reconstruct an element of a Hilbert space in t...
Abstract. The primary goal of this paper is to develop fast algorithms for signal reconstruction fro...
A host of problems involve the recovery of structured signals from a dimensionality reduced represen...
The research reported in this dissertation addresses the reconstruction of signals and images from l...
International audienceIn many linear inverse problems, we want to estimate an unknown vector belongi...
The function estimation in RKHS (Reproducing Kernel Hilbert Space) from finite noisy samples is a ty...
This paper focuses on the reconstruction of the signal structure of a system in the presence of nois...
We consider signal reconstruction from the norms of subspace components generalizing standard phase ...
Abstract The goal of this paper is to develop fast algorithms for signal reconstruc-tion from magnit...
The problem is to reconstruct a function f: RD → C from finitely many linear functional values. To m...
Phase retrieval in real or complex Hilbert spaces is the task of recovering a vector, up to an overa...
The last decade witnessed the burgeoning development in the reconstruction of signals by exploiting ...
In this paper we consider the classical problem of blind deconvolution of multiple signals from its ...
Abstract. This paper is concerned with the question of reconstructing a vector in a finite-dimension...
In this paper we present a signal reconstruction algorithm from absolute value of frame coefficients...
AbstractWe introduce a simple and efficient method to reconstruct an element of a Hilbert space in t...
Abstract. The primary goal of this paper is to develop fast algorithms for signal reconstruction fro...
A host of problems involve the recovery of structured signals from a dimensionality reduced represen...
The research reported in this dissertation addresses the reconstruction of signals and images from l...
International audienceIn many linear inverse problems, we want to estimate an unknown vector belongi...
The function estimation in RKHS (Reproducing Kernel Hilbert Space) from finite noisy samples is a ty...
This paper focuses on the reconstruction of the signal structure of a system in the presence of nois...
We consider signal reconstruction from the norms of subspace components generalizing standard phase ...
Abstract The goal of this paper is to develop fast algorithms for signal reconstruc-tion from magnit...
The problem is to reconstruct a function f: RD → C from finitely many linear functional values. To m...
Phase retrieval in real or complex Hilbert spaces is the task of recovering a vector, up to an overa...
The last decade witnessed the burgeoning development in the reconstruction of signals by exploiting ...
In this paper we consider the classical problem of blind deconvolution of multiple signals from its ...