WARPd: a linearly convergent first-order primal-dual algorithm for inverse problems with approximate sharpness conditions
MJ Colbrook - SIAM Journal on Imaging Sciences, 2022 - SIAM
Sharpness conditions directly control the recovery performance of restart schemes for first-
order optimization methods without the need for restrictive assumptions such as strong …
order optimization methods without the need for restrictive assumptions such as strong …
NESTANets: Stable, accurate and efficient neural networks for analysis-sparse inverse problems
Solving inverse problems is a fundamental component of science, engineering and
mathematics. With the advent of deep learning, deep neural networks have significant …
mathematics. With the advent of deep learning, deep neural networks have significant …
Discretization of non-uniform rational B-spline (NURBS) models for meshless isogeometric analysis
We present an algorithm for fast generation of quasi-uniform and variable-spacing nodes on
domains whose boundaries are represented as computer-aided design (CAD) models, more …
domains whose boundaries are represented as computer-aided design (CAD) models, more …
Restarts subject to approximate sharpness: A parameter-free and optimal scheme for first-order methods
Sharpness is an almost generic assumption in continuous optimization that bounds the
distance from minima by objective function suboptimality. It leads to the acceleration of first …
distance from minima by objective function suboptimality. It leads to the acceleration of first …
Signal reconstruction from blind compressive measurements using Procrustes method
The reconstruction of signals from their blind compressed measurements is a highly ill-
posed problem because the representing basis is unknown. This paper proposes an …
posed problem because the representing basis is unknown. This paper proposes an …
Enhanced total variation minimization for stable image reconstruction
The total variation (TV) regularization has phenomenally boosted various variational models
for image processing tasks. We propose to combine the backward diffusion process in the …
for image processing tasks. We propose to combine the backward diffusion process in the …
Reconstruction of signals from their blind compressive measurements
This paper proposes a method to reconstruct a signal from its Blind Compressive
measurements by formulating it as a constrained optimization problem. It considers two …
measurements by formulating it as a constrained optimization problem. It considers two …
Approximation Theory of Total Variation Minimization for Data Completion
JF Cai, JK Choi, K Wei - arxiv preprint arxiv:2207.07473, 2022 - arxiv.org
Total variation (TV) minimization is one of the most important techniques in modern
signal/image processing, and has wide range of applications. While there are numerous …
signal/image processing, and has wide range of applications. While there are numerous …
[PDF][PDF] Provably Accurate, Stable and Efficient Deep Neural Networks for Compressive Imaging
M Neyra-Nesterenko, B Adcock - ps-mathematik.univie.ac.at
Machine learning, and in particular, Deep Learning (DL), has recently demonstrated
substantial potential to outperform standard methods in the field of compressive imaging; …
substantial potential to outperform standard methods in the field of compressive imaging; …
[PDF][PDF] A Complete Bibliography of Publications in SIAM Journal on Imaging Sciences
NHF Beebe - 2024 - ctan.math.utah.edu
Based [ABG+13c, ACSW12, AT11, BS21b, BQ22, BAA14, BPP22, BCP13a, BS15, BH12,
BH15a, BH15b, BEFL21, COS09, CCMS13, CTM+24, CGMP11, CLL11, CTY13, CTWY15 …
BH15a, BH15b, BEFL21, COS09, CCMS13, CTM+24, CGMP11, CLL11, CTY13, CTWY15 …