62 citations · 124 across the 14 of their papers we have counts for
15 papers
A Hybrid Quantum Neural Network to Analyse Big Experimental Powder X-ray Diffraction Data
H. Dong, S. D. M. Jacques, M. Q. Hlatshwayo +4
Quantitative analysis of experimental powder X-ray diffraction data remains challenging when evaluating complex multiphase materials and noisy measurements. We introduce a hybrid q…
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 (…
Split, Skip and Play: Variance-Reduced ProxSkip for Tomography Reconstruction is Extremely Fast
Evangelos Papoutsellis, Zeljko Kereta, Kostas Papafitsoros
Many modern iterative solvers for large-scale tomographic reconstruction incur two major computational costs per iteration: expensive forward/adjoint projections to update the data…
Why do we regularise in every iteration for imaging inverse problems?
Evangelos Papoutsellis, Zeljko Kereta, Kostas Papafitsoros
Regularisation is commonly used in iterative methods for solving imaging inverse problems. Many algorithms involve the evaluation of the proximal operator of the regularisation ter…
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…
A directional regularization method for the limited-angle Helsinki Tomography Challenge using the Core Imaging Library (CIL)
Jakob Sauer Jørgensen, Evangelos Papoutsellis, Laura Murgatroyd +2
This article presents the algorithms developed by the Core Imaging Library (CIL) developer team for the Helsinki Tomography Challenge 2022. The challenge focused on reconstructing…
Unrolled three-operator splitting for parameter-map learning in Low Dose X-ray CT reconstruction
Andreas Kofler, Fabian Altekrüger, Fatima Antarou Ba +6
We propose a method for fast and automatic estimation of spatially dependent regularization maps for total variation-based (TV) tomography reconstruction. The estimation is based o…
Learning Regularization Parameter-Maps for Variational Image Reconstruction using Deep Neural Networks and Algorithm Unrolling
Andreas Kofler, Fabian Altekrüger, Fatima Antarou Ba +6
We introduce a method for fast estimation of data-adapted, spatio-temporally dependent regularization parameter-maps for variational image reconstruction, focusing on total variati…
Enhanced hyperspectral tomography for bioimaging by spatiospectral reconstruction
Ryan Warr, Evelina Ametova, Robert J. Cernik +6
Here we apply hyperspectral bright field imaging to collect computed tomographic images with excellent energy resolution (800 eV), applying it for the first time to map the distrib…
Crystalline phase discriminating neutron tomography using advanced reconstruction methods
Evelina Ametova, Genoveva Burca, Suren Chilingaryan +8
Time-of-flight neutron imaging offers complementary attenuation contrast to X-ray computed tomography (CT), coupled with the ability to extract additional information from the vari…
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…
Core Imaging Library -- Part I: a versatile Python framework for tomographic imaging
Jakob S. Jørgensen, Evelina Ametova, Genoveva Burca +8
We present the Core Imaging Library (CIL), an open-source Python framework for tomographic imaging with particular emphasis on reconstruction of challenging datasets. Conventional…
Infimal Convolution Regularisation Functionals of BV and Spaces. The Case p
Martin Burger, Konstantinos Papafitsoros, Evangelos Papoutsellis +1
In this paper we analyse an infimal convolution type regularisation functional called , based on the total variation () and the $\mathrm{L}^{\in…
Infimal convolution regularisation functionals of BV and spaces. Part I: The finite case
Martin Burger, Konstantinos Papafitsoros, Evangelos Papoutsellis +1
We study a general class of infimal convolution type regularisation functionals suitable for applications in image processing. These functionals incorporate a combination of the to…
Total Variation Regularisation in Measurement and Image space for PET reconstruction
Martin Burger, Jahn Müller, Evangelos Papoutsellis +1
The aim of this paper is to test and analyze a novel technique for image reconstruction in positron emission tomography, which is based on (total variation) regularization on both…