Playing with duality: An overview of recent primal? dual approaches for solving large-scale optimization problems
N Komodakis, JC Pesquet - IEEE Signal Processing Magazine, 2015 - ieeexplore.ieee.org
Optimization methods are at the core of many problems in signal/image processing,
computer vision, and machine learning. For a long time, it has been recognized that looking …
computer vision, and machine learning. For a long time, it has been recognized that looking …
An introduction to continuous optimization for imaging
A Chambolle, T Pock - Acta Numerica, 2016 - cambridge.org
A large number of imaging problems reduce to the optimization of a cost function, with
typical structural properties. The aim of this paper is to describe the state of the art in …
typical structural properties. The aim of this paper is to describe the state of the art in …
Accelerated high-resolution photoacoustic tomography via compressed sensing
Current 3D photoacoustic tomography (PAT) systems offer either high image quality or high
frame rates but are not able to deliver high spatial and temporal resolution simultaneously …
frame rates but are not able to deliver high spatial and temporal resolution simultaneously …
Convolutional proximal neural networks and plug-and-play algorithms
In this paper, we introduce convolutional proximal neural networks (cPNNs), which are by
construction averaged operators. For filters with full length, we propose a stochastic gradient …
construction averaged operators. For filters with full length, we propose a stochastic gradient …
On the adjoint operator in photoacoustic tomography
Photoacoustic tomography (PAT) is an emerging biomedical imaging from coupled physics
technique, in which the image contrast is due to optical absorption, but the information is …
technique, in which the image contrast is due to optical absorption, but the information is …
Parseval proximal neural networks
The aim of this paper is twofold. First, we show that a certain concatenation of a proximity
operator with an affine operator is again a proximity operator on a suitable Hilbert space …
operator with an affine operator is again a proximity operator on a suitable Hilbert space …
PDE-based group equivariant convolutional neural networks
We present a PDE-based framework that generalizes Group equivariant Convolutional
Neural Networks (G-CNNs). In this framework, a network layer is seen as a set of PDE …
Neural Networks (G-CNNs). In this framework, a network layer is seen as a set of PDE …
Wavelet-based image deconvolution and reconstruction
N Pustelnik, A Benazza-Benhayia, Y Zheng… - Wiley encyclopedia of …, 2016 - hal.science
Image deconvolution and reconstruction are inverse problems which are encountered in a
wide array of applications. Due to the ill-posedness of such problems, their resolution …
wide array of applications. Due to the ill-posedness of such problems, their resolution …
Backtracking strategies for accelerated descent methods with smooth composite objectives
L Calatroni, A Chambolle - SIAM journal on optimization, 2019 - SIAM
We present and analyze a backtracking strategy for a general fast iterative shrinkage/
thresholding algorithm proposed by Chambolle and Pock [Acta Numer., 25 (2016), pp. 161 …
thresholding algorithm proposed by Chambolle and Pock [Acta Numer., 25 (2016), pp. 161 …
Extended randomized Kaczmarz method for sparse least squares and impulsive noise problems
Abstract The Extended Randomized Kaczmarz method is a well known iterative scheme
which can find the Moore-Penrose inverse solution of a possibly inconsistent linear system …
which can find the Moore-Penrose inverse solution of a possibly inconsistent linear system …