We study the sparse phase retrieval problem, which aims to recover a spa...
In recent years, patch-based image restoration approaches have demonstra...
We investigate a generalized framework for estimating latent low-rank te...
High-dimensional linear regression under heavy-tailed noise or outlier
c...
This paper investigates the spectral norm version of the column subset
s...
We study the sparse phase retrieval problem, recovering an s-sparse
leng...
The finite-rate-of-innovation (FRI) framework which corresponds a
signal...
Total variation (TV) minimization is one of the most important technique...
The phase retrieval problem is concerned with recovering an unknown sign...
We study the robust matrix completion problem for the low-rank Hankel ma...
Low-rank matrix estimation under heavy-tailed noise is challenging, both...
A fundamental problem in phase retrieval is to reconstruct an unknown si...
A fundamental task in phase retrieval is to recover an unknown signal ∈ ...
In this work we propose a nonconvex two-stage stochastic
alternating min...
We study the problem of estimating precision matrices in multivariate
Ga...
The tensor train (TT) format enjoys appealing advantages in handling
str...
We investigate a generalized framework to estimate a latent low-rank plu...
Recently, mapping a signal/image into a low rank Hankel/Toeplitz matrix ...
We consider the problem of recovering a low-multilinear-rank tensor from...
In this paper, we consider the sparse phase retrival problem, recovering...
Recently, the finite-rate-of-innovation (FRI) based continuous domain
re...
Consider a spectrally sparse signal x that consists of r
complex sinusoi...
We investigate the sample size requirement for exact recovery of a high ...
The Cadzow's algorithm is a signal denoising and recovery method which w...
Removing undesired reflections from images taken through the glass is of...
Random projections are able to perform dimension reduction efficiently f...
The cryo-electron microscope (cryo-EM) is increasingly popular these yea...
The problem of finding a vector x which obeys a set of quadratic equatio...
Low rank model arises from a wide range of applications, including machi...
Signals are generally modeled as a superposition of exponential function...
Characterizing the phase transitions of convex optimizations in recoveri...
Compressed sensing has shown great potentials in accelerating magnetic
r...
Objective: Improve the reconstructed image with fast and multi-class
dic...
This paper explores robust recovery of a superposition of R distinct
com...
Recent research in off-the-grid compressed sensing (CS) has demonstrated...
In this paper, we consider using total variation minimization to recover...
We consider the problem of estimating the inverse covariance matrix by
m...