A comprehensive survey of sparse regularization: Fundamental, state-of-the-art methodologies and applications on fault diagnosis
Q Li - Expert Systems with Applications, 2023 - Elsevier
Sparse regularization has been attracting much attention in industrial applications over the
past few decades. By exploiting the latent data structure in low-dimensional subspaces, a …
past few decades. By exploiting the latent data structure in low-dimensional subspaces, a …
Adaptive total variation image deblurring: a majorization–minimization approach
This paper presents a new approach to image deconvolution (deblurring), under total
variation (TV) regularization, which is adaptive in the sense that it does not require the user …
variation (TV) regularization, which is adaptive in the sense that it does not require the user …
Proximal algorithms
This monograph is about a class of optimization algorithms called proximal algorithms. Much
like Newton's method is a standard tool for solving unconstrained smooth optimization …
like Newton's method is a standard tool for solving unconstrained smooth optimization …
Majorization-minimization algorithms in signal processing, communications, and machine learning
This paper gives an overview of the majorization-minimization (MM) algorithmic framework,
which can provide guidance in deriving problem-driven algorithms with low computational …
which can provide guidance in deriving problem-driven algorithms with low computational …
Enhancing Sparsity by Reweighted ℓ 1 Minimization
It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what
appear to be highly incomplete sets of linear measurements and (2) that this can be done by …
appear to be highly incomplete sets of linear measurements and (2) that this can be done by …
From sparse solutions of systems of equations to sparse modeling of signals and images
A full-rank matrix \bfA∈R^n*m with n<m generates an underdetermined system of linear
equations \bfAx=\bfb having infinitely many solutions. Suppose we seek the sparsest …
equations \bfAx=\bfb having infinitely many solutions. Suppose we seek the sparsest …
A new TwIST: Two-step iterative shrinkage/thresholding algorithms for image restoration
JM Bioucas-Dias… - IEEE Transactions on …, 2007 - ieeexplore.ieee.org
Iterative shrinkage/thresholding (1ST) algorithms have been recently proposed to handle a
class of convex unconstrained optimization problems arising in image restoration and other …
class of convex unconstrained optimization problems arising in image restoration and other …
Sparse reconstruction by separable approximation
Finding sparse approximate solutions to large underdetermined linear systems of equations
is a common problem in signal/image processing and statistics. Basis pursuit, the least …
is a common problem in signal/image processing and statistics. Basis pursuit, the least …
Fast image recovery using variable splitting and constrained optimization
MV Afonso, JM Bioucas-Dias… - IEEE transactions on …, 2010 - ieeexplore.ieee.org
We propose a new fast algorithm for solving one of the standard formulations of image
restoration and reconstruction which consists of an unconstrained optimization problem …
restoration and reconstruction which consists of an unconstrained optimization problem …
Bregman Iterative Algorithms for -Minimization with Applications to Compressed Sensing
We propose simple and extremely efficient methods for solving the basis pursuit problem
\min{‖u‖_1:Au=f,u∈R^n\}, which is used in compressed sensing. Our methods are based …
\min{‖u‖_1:Au=f,u∈R^n\}, which is used in compressed sensing. Our methods are based …