scispace - formally typeset
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.

Papers
More filters
Journal ArticleDOI

Fast and flexible X-ray tomography using the ASTRA toolbox.

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

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

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.