K
K. Joost Batenburg
Researcher at Centrum Wiskunde & Informatica
Publications - 40
Citations - 1325
K. Joost Batenburg is an academic researcher from Centrum Wiskunde & Informatica. The author has contributed to research in topics: Tomographic reconstruction & Iterative reconstruction. The author has an hindex of 12, co-authored 40 publications receiving 911 citations. Previous affiliations of K. Joost Batenburg include Argonne National Laboratory & Leiden University.
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Journal ArticleDOI
Fast and flexible X-ray tomography using the ASTRA toolbox.
Wim van Aarle,Willem Jan Palenstijn,Jeroen Cant,Eline Janssens,Folkert Bleichrodt,Andrei Dabravolski,Jan De Beenhouwer,K. Joost Batenburg,Jan Sijbers +8 more
TL;DR: Aimed at researchers across multiple tomographic application fields, the ASTRA Toolbox provides a highly efficient and highly flexible open source set of tools for tomographic projection and reconstruction.
Journal ArticleDOI
Measuring Lattice Strain in Three Dimensions through Electron Microscopy
Bart Goris,Jan De Beenhouwer,Annick De Backer,Daniele Zanaga,K. Joost Batenburg,Ana Sánchez-Iglesias,Luis M. Liz-Marzán,Sandra Van Aert,Sara Bals,Jan Sijbers,Gustaaf Van Tendeloo +10 more
TL;DR: This work proposes a novel model-based approach from which atomic coordinates are measured in Au nanodecahedra using electron tomography, demonstrating the importance of investigating lattice strain in 3D.
The ASTRA Tomography Toolbox
TL;DR: This work describes how the design of the ASTRA toolbox allows for full exibility in specifying the geometry while still maintaining an ecient mapping onto the underlying hardware.
Journal ArticleDOI
TomoBank: a tomographic data repository for computational x-ray science
Francesco De Carlo,Doǧa Gürsoy,Daniel J. Ching,K. Joost Batenburg,Wolfgang Ludwig,Lucia Mancini,Federica Marone,Rajmund Mokso,Daniël M. Pelt,Jan Sijbers,Mark L. Rivers +10 more
TL;DR: The x-ray tomography data bank, tomoBank, provides a repository of experimental and simulated datasets with the aim to foster collaboration among computational scientists, beamline scientists, and experimentalists and to accelerate the development and implementation of tomographic reconstruction methods for synchrotron facility production software by providing easy access to challenging datasets and their descriptors.
Journal ArticleDOI
Noise2Inverse: Self-Supervised Deep Convolutional Denoising for Tomography
TL;DR: Noise2Inverse is proposed, a deep CNN-based denoising method for linear image reconstruction algorithms that does not require any additional clean or noisy data and demonstrates an improvement in peak signal-to-noise ratio and structural similarity index compared to state-of-the-art image Denoising methods, and conventional reconstruction methods, such as Total-Variation Minimization.