Finding robust minimizer for non-convex phase retrieval
The phase retrieval task has received considerable attention in recent years. Here, phase
retrieval refers to the problem of recovering the clear image from magnitude-only data of its …
retrieval refers to the problem of recovering the clear image from magnitude-only data of its …
Low-rank phase retrieval with structured tensor models
We study the low-rank phase retrieval problem, where the objective is to recover a sequence
of signals (typically images) given the magnitude of linear measurements of those signals …
of signals (typically images) given the magnitude of linear measurements of those signals …
Fourier phase retrieval with extended support estimation via deep neural network
We consider the problem of sparse phase retrieval from Fourier transform magnitudes to
recover the-sparse signal vector and its support. We exploit extended support estimate with …
recover the-sparse signal vector and its support. We exploit extended support estimate with …
Iterative reweighted DOA estimation for impulsive noise processing based on off-grid variational Bayesian learning
The performance of acoustic source localization with array system is limited by impulsive
noise such as electromagnetic interference, car ignitions, bursting, and so on. The impulsive …
noise such as electromagnetic interference, car ignitions, bursting, and so on. The impulsive …
Structured Phase Retrieval-aided Channel Estimation for Millimeter-Wave/Sub-Terahertz MIMO Systems
KH Liu, X Li, H Zhao, G Fan - 2022 IEEE 96th Vehicular …, 2022 - ieeexplore.ieee.org
We study the question of phase retrieval aided Millimeter wave (mmWave) channel
estimation and propose a sparse phase retrieval-aided mmWave channel estimation …
estimation and propose a sparse phase retrieval-aided mmWave channel estimation …
Phase Retrieval by Tensor Total Least Squares
J Liu, C Zhu, Y Chen, X Huang… - ICASSP 2024-2024 IEEE …, 2024 - ieeexplore.ieee.org
Phase retrieval seeks to reconstruct a series of image sequences from measurements that
only capture their magnitudes. Current approaches either flatten and stack the image …
only capture their magnitudes. Current approaches either flatten and stack the image …
Robust initialization estimator and its application in solving quadratic equations
K Liu, L Yang, J Xie - IEEE Access, 2019 - ieeexplore.ieee.org
In many non-convex optimization-based signal recovery tasks, a good initial point is
essential for the performance of the optimization process. One seeks to start the point from a …
essential for the performance of the optimization process. One seeks to start the point from a …
Fast Quadratic Sensing Via Nonconvex Optimization
K Liu, L Wan, L Sun - 2022 30th European Signal Processing …, 2022 - ieeexplore.ieee.org
This paper concerns the problem of recovering an unknown but rank-r signal from quadratic
measurements. We formulate the recovery problem as two-stage nonconvex optimization …
measurements. We formulate the recovery problem as two-stage nonconvex optimization …
[PDF][PDF] Iterative X-ray spectroscopic ptychography
H Chang, Z Rong, P Enfedaque… - Journal of applied …, 2020 - journals.iucr.org
Spectroscopic ptychography is a powerful technique to determine the chemical composition
of a sample with high spatial resolution. In spectro-ptychography, a sample is rastered …
of a sample with high spatial resolution. In spectro-ptychography, a sample is rastered …
Optimization Problems with Low-Dimensional Tensor Structure
SM Kwon - 2022 - search.proquest.com
In this thesis, we consider optimization problems that involve statistically estimating signals
from tensor data. We observe that modelling the signals as tensors using tensor …
from tensor data. We observe that modelling the signals as tensors using tensor …