Blind gain and phase calibration via sparse spectral methods
Blind gain and phase calibration (BGPC) is a bilinear inverse problem involving the
determination of unknown gains and phases of the sensing system, and the unknown signal …
determination of unknown gains and phases of the sensing system, and the unknown signal …
Computing high quality phase-only holograms for holographic displays
Holography has demonstrated potential to achieve a wide field of view, focus supporting,
optical see-through augmented reality display in an eyeglasses form factor. Although phase …
optical see-through augmented reality display in an eyeglasses form factor. Although phase …
The recovery of complex sparse signals from few phaseless measurements
Y Xia, Z Xu - Applied and Computational Harmonic Analysis, 2021 - Elsevier
We study the stable recovery of complex k-sparse signals from as few phaseless
measurements as possible. The main result is to show that one can employ ℓ 1 minimization …
measurements as possible. The main result is to show that one can employ ℓ 1 minimization …
A Regularized Weighted Smoothed L0 Norm Minimization Method for Underdetermined Blind Source Separation
L Wang, X Yin, H Yue, J Xiang - Sensors, 2018 - mdpi.com
Compressed sensing (CS) theory has attracted widespread attention in recent years and
has been widely used in signal and image processing, such as underdetermined blind …
has been widely used in signal and image processing, such as underdetermined blind …
Non-locally coded Fourier-transform ghost imaging
A non-locally coded Fourier-transform ghost imaging (FGI) scheme and relevant coded
phase retrieval method have been proposed to improve the image quality in ghost imaging …
phase retrieval method have been proposed to improve the image quality in ghost imaging …
Sample-efficient sparse phase retrieval via stochastic alternating minimization
In this work, we propose a nonconvex stochastic alternating minimizing (SAM) method for
sparse phase retrieval, where an-sparse vector of length is recovered from phaseless linear …
sparse phase retrieval, where an-sparse vector of length is recovered from phaseless linear …
Fundamental limits of phasemax for phase retrieval: A replica analysis
O Dhifallah, YM Lu - 2017 IEEE 7th International Workshop on …, 2017 - ieeexplore.ieee.org
We consider a recently proposed convex formulation, known as the PhaseMax method, for
solving the phase retrieval problem. Using the replica method from statistical mechanics, we …
solving the phase retrieval problem. Using the replica method from statistical mechanics, we …
Learning without the phase: Regularized phasemax achieves optimal sample complexity
F Salehi, E Abbasi, B Hassibi - Advances in Neural …, 2018 - proceedings.neurips.cc
The problem of estimating an unknown signal, $\mathbf x_0\in\mathbb R^ n $, from a vector
$\mathbf y\in\mathbb R^ m $ consisting of $ m $ magnitude-only measurements of the form …
$\mathbf y\in\mathbb R^ m $ consisting of $ m $ magnitude-only measurements of the form …
Signal reconstruction from modulo observations
We consider the problem of reconstructing a signal from under-determined modulo
observations (or measurements). This observation model is inspired by a (relatively) less …
observations (or measurements). This observation model is inspired by a (relatively) less …
Deep S3PR: Simultaneous Source Separation and Phase Retrieval Using Deep Generative Models
CA Metzler, G Wetzstein - ICASSP 2021-2021 IEEE …, 2021 - ieeexplore.ieee.org
This paper introduces and solves the simultaneous source separation and phase retrieval (S
3 PR) problem. S 3 PR is an important but largely unsolved problem in a number application …
3 PR) problem. S 3 PR is an important but largely unsolved problem in a number application …