38 citations · 42 across the 8 of their papers we have counts for
10 papers
A Modular Approach to Stochastic Optimisation for Inverse Problems Using the Core Imaging Library
Evangelos Papoutsellis, Margaret A. G. Duff, Jakob S. Jørgensen +5
The Core Imaging Library (CIL) is an open-source versatile Python framework for solving inverse problems with special emphasis on imaging applications such as computed tomography (…
Accelerated Convergent Motion Compensated Image Reconstruction
Claire Delplancke, Kris Thielemans, Matthias J. Ehrhardt
Motion correction aims to prevent motion artefacts which may be caused by respiration, heartbeat, or head movements for example. In a preliminary step, the measured data is divided…
Global Optimization Algorithm through High-Resolution Sampling
Daniel Cortild, Claire Delplancke, Nadia Oudjane +1
We present an optimization algorithm that can identify a global minimum of a potentially nonconvex smooth function with high probability, assuming the Gibbs measure of the potentia…
Stochastic Optimisation Framework using the Core Imaging Library and Synergistic Image Reconstruction Framework for PET Reconstruction
Evangelos Papoutsellis, Casper da Costa-Luis, Daniel Deidda +10
We introduce a stochastic framework into the open--source Core Imaging Library (CIL) which enables easy development of stochastic algorithms. Five such algorithms from the literatu…
Stochastic Primal Dual Hybrid Gradient Algorithm with Adaptive Step-Sizes
Antonin Chambolle, Claire Delplancke, Matthias J. Ehrhardt +2
In this work we propose a new primal-dual algorithm with adaptive step-sizes. The stochastic primal-dual hybrid gradient (SPDHG) algorithm with constant step-sizes has become widel…
On the convergence and sampling of randomized primal-dual algorithms and their application to parallel MRI reconstruction
Eric B Gutierrez, Claire Delplancke, Matthias J Ehrhardt
Stochastic Primal-Dual Hybrid Gradient (SPDHG) is an algorithm proposed by Chambolle et al. (2018) to efficiently solve a wide class of nonsmooth large-scale optimization problems.…
Core Imaging Library -- Part II: Multichannel reconstruction for dynamic and spectral tomography
Evangelos Papoutsellis, Evelina Ametova, Claire Delplancke +7
The newly developed Core Imaging Library (CIL) is a flexible plug and play library for tomographic imaging with a specific focus on iterative reconstruction. CIL provides building…
Convergence Properties of a Randomized Primal-Dual Algorithm with Applications to Parallel MRI
Eric B. Gutierrez, Claire Delplancke, Matthias J. Ehrhardt
The Stochastic Primal-Dual Hybrid Gradient (SPDHG) was proposed by Chambolle et al. (2018) and is an efficient algorithm to solve some nonsmooth large-scale optimization problems.…
Berry-Esseen bounds for the chi-square distance in the Central Limit Theorem: a Markovian approach
Claire Delplancke, Laurent Miclo
This article presents a new proof of the rate of convergence to the normal distribution of sums of independent, identically distributed random variables in chi-square distance, whi…
Intertwinings and Stein's magic factors for birth-death processes
Bertrand Cloez, Claire Delplancke
This article investigates second order intertwinings between semigroups of birth-death processes and discrete gradients on the space of natural integers N. It goes one step beyond…