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相关论文: Signal Reconstruction from Phase-only Measurements…

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The recovery of an unknown signal from its linear measurements is a fundamental problem spanning numerous scientific and engineering disciplines. Commonly, prior knowledge suggests that the underlying signal resides within a known algebraic…

信息论 · 计算机科学 2025-06-27 Zhiqiang Xu

We consider the problem of sparse phase retrieval, where a $k$-sparse signal ${\bf x} \in {\mathbb R}^n \textrm{ (or } {\mathbb C}^n\textrm{)}$ is measured as ${\bf y} = |{\bf Ax}|,$ where ${\bf A} \in {\mathbb R}^{m \times n} \textrm{ (or…

信息论 · 计算机科学 2014-08-18 Mehmet Akçakaya , Vahid Tarokh

Phase retrieval is to recover the signals from phaseless measurements which is raised in many areas. A fundamental problem in phase retrieval is to determine the minimal measurement number $m$ so that one can recover $d$-dimensional signals…

信息论 · 计算机科学 2017-07-06 Zhiqiang Xu

We study the problem of recovering a structured signal from independently and identically drawn linear measurements. A convex penalty function $f(\cdot)$ is considered which penalizes deviations from the desired structure, and signal…

统计理论 · 数学 2019-06-21 Ehsan Abbasi , Fariborz Salehi , Babak Hassibi

In this paper, we develop a framework of generalized phase retrieval in which one aims to reconstruct a vector ${\mathbf x}$ in ${\mathbb R}^d$ or ${\mathbb C}^d$ through quadratic samples ${\mathbf x}^*A_1{\mathbf x}, \dots, {\mathbf…

信息论 · 计算机科学 2016-06-06 Yang Wang , Zhiqiang Xu

In many applications, signals are measured according to a linear process, but the phases of these measurements are often unreliable or not available. To reconstruct the signal, one must perform a process known as phase retrieval. This paper…

泛函分析 · 数学 2013-07-30 Matthew Fickus , Dustin G. Mixon , Aaron A. Nelson , Yang Wang

A signal is sparse in one of its representation domain if the number of nonzero coefficients in that domain is much smaller than the total number of coefficients. Sparse signals can be reconstructed from a very reduced set of…

信息论 · 计算机科学 2017-06-19 Ljubisa Stankovic , Milos Dakovic , Srdjan Stankovic , Irena Orovic

The problem of recovering a signal from its phaseless Fourier transform measurements, called Fourier phase retrieval, arises in many applications in engineering and science. Fourier phase retrieval poses fundamental theoretical and…

信息论 · 计算机科学 2017-11-08 Tamir Bendory , Robert Beinert , Yonina C. Eldar

Phase retrieval problems in antenna measurements arise when a reference phase cannot be provided to all measurement locations. Phase retrieval algorithms require sufficiently many independent measurement samples of the radiated fields to be…

信号处理 · 电气工程与系统科学 2022-06-24 Josef Knapp , Alexander Paulus , Jonas Kornprobst , Uwe Siart , Thomas F. Eibert

We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by a (relatively) less well-known imaging mechanism called modulo imaging, which can be used…

机器学习 · 统计学 2019-07-18 Viraj Shah , Chinmay Hegde

In phase retrieval we want to recover an unknown signal $\boldsymbol x\in\mathbb C^d$ from $n$ quadratic measurements of the form $y_i = |\langle{\boldsymbol a}_i,{\boldsymbol x}\rangle|^2+w_i$ where $\boldsymbol a_i\in \mathbb C^d$ are…

机器学习 · 统计学 2018-07-27 Marco Mondelli , Andrea Montanari

Recovering an unknown complex signal from the magnitude of linear combinations of the signal is referred to as phase retrieval. We present an exact performance analysis of a recently proposed convex-optimization-formulation for this…

信息论 · 计算机科学 2018-01-23 Fariborz Salehi , Ehsan Abbasi , Babak Hassibi

Phase retrieval is in general a non-convex and non-linear task and the corresponding algorithms struggle with the issue of local minima. We consider the case where the measurement samples within typically very small and disconnected subsets…

信号处理 · 电气工程与系统科学 2022-06-28 Jonas Kornprobst , Alexander Paulus , Josef Knapp , Thomas F. Eibert

The goal of phase-only compressed sensing is to recover a structured signal $\mathbf{x}$ from the phases $\mathbf{z} = {\rm sign}(\mathbf{\Phi}\mathbf{x})$ under some complex-valued sensing matrix $\mathbf{\Phi}$. Exact reconstruction of…

信息论 · 计算机科学 2025-01-22 Junren Chen , Lexiao Lai , Arian Maleki

Suppose we wish to recover a signal x in C^n from m intensity measurements of the form |<x,z_i>|^2, i = 1, 2,..., m; that is, from data in which phase information is missing. We prove that if the vectors z_i are sampled independently and…

信息论 · 计算机科学 2011-09-22 Emmanuel J. Candes , Thomas Strohmer , Vladislav Voroninski

In this paper, we consider the phase retrieval problem in which one aims to recover a signal from the magnitudes of affine measurements. Let $\{{\mathbf a}_j\}_{j=1}^m \subset {\mathbb H}^d$ and ${\mathbf b}=(b_1, \ldots,…

信息论 · 计算机科学 2016-08-23 Bing Gao , Qiyu Sun , Yang Wang , Zhiqiang Xu

The problem of recovering a pair of signals from their blind phaseless short-time Fourier transform measurements arises in several important phase retrieval applications, including ptychography and ultra-short pulse characterization. In…

信息论 · 计算机科学 2019-04-11 Tamir Bendory , Dan Edidin , Yonina C. Eldar

In a variety of fields, in particular those involving imaging and optics, we often measure signals whose phase is missing or has been irremediably distorted. Phase retrieval attempts the recovery of the phase information of a signal from…

信息论 · 计算机科学 2013-09-27 Juri Ranieri , Amina Chebira , Yue M. Lu , Martin Vetterli

We consider the problem of recovering a signal $\mathbf{x}^* \in \mathbf{R}^n$, from magnitude-only measurements $y_i = |\left\langle\mathbf{a}_i,\mathbf{x}^*\right\rangle|$ for $i=[m]$. Also called the phase retrieval, this is a…

机器学习 · 统计学 2017-11-28 Gauri Jagatap , Chinmay Hegde

The one-dimensional phase retrieval problem consists in the recovery of a complex-valued signal from its Fourier intensity. Due to the well-known ambiguousness of this problem, the determination of the original signal within the extensive…

数值分析 · 数学 2021-03-19 Robert Beinert
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