Description
Leader of the group Computational Imaging: Joost Batenburg.
Our research group is developing the next generation of 3D imaging – enabling scientists to look further into objects of all kinds. Based on mathematics, algorithms and numerical solution techniques, our approach is interdisciplinary, combining aspects of mathematics, computer science and physics. We pride ourselves on the versatility of our solutions, and our algorithms can be applied to a wide range of imaging in science, medicine and industry. In Computational Imaging, it’s our goal to constantly push the boundaries of research. By combining advanced image acquisition, parameter estimation, and discrete tomography algorithms for example, we are able to develop workflows for 3D electron microscopy at atomic resolution.
Vacancies
No vacancies currently.
News
CWI develops new algebraic filter for reconstruction of X-Ray images
CWI PhD student Linda Plantagie of the Computational Imaging research group has introduced a new algebraic filter for the use of reconstructing X-Ray images. Linda Plantagie will defend her thesis 13 April at Leiden University.
The Netherlands’ smallest supercomputer is here
A team of Dutch scientists has built a supercomputer the size of four pizza boxes. The Little Green Machine II has the computing power of 10,000 PCs and will be used by researchers in oceanography, computer science, artificial intelligence, financial modeling and astronomy. CWI researchers Joost Batenburg and Kees Oosterlee, who were part of the development team, will use the machine for computational imaging and machine learning for time series respectively. The computer is based at Leiden University (the Netherlands) and developed with help from IBM.
ERCIM News 108 on Computational Imaging co-coordinated by Joost Batenburg
In January 2017, ERCIM News No. 108 was published: http://ercim-news.ercim.eu/en108. It features a special theme on Computational Imaging, coordinated by guest editors Joost Batenburg (CWI) and Tamas Sziranyi (MTA SZTAKI).
Faster computation of high-quality 3D images
PhD student Daniël Pelt of CWI has developed new algorithms for computer tomography (CT) for fast computation of high-quality three-dimensional images. This increases the practical use of this technology in areas such as material sciences and biomedical research.
Members
- Vladyslav Andriiashen
- Joost Batenburg
- Francien Bossema
- Viviane Desgrange
- Hamid Fathi
- Adriaan Graas
- Maximilian Kiss
- Tristan van Leeuwen
- Robert van Liere
- Felix Lucka
- Jan Matula
- Willem Jan Palenstijn
- Richard Schoonhoven
- Dirk Schut
- Dzemila Sero
- Alexander Skorikov
- Roozbeh Valadian
- Tianyuan Wang
- Mathé Zeegers
Associated Members
Publications
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Hendriksen, A.A, Schut, D, Palenstijn, W.J, Viganò, N.R, Kim, J, Pelt, D.M, … Batenburg, K.J. (2021). Tomosipo: fast, flexible, and convenient 3D tomography for complex scanning geometries in Python. doi:10.1364/OE.439909
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Pilikos, G, Horchens, L, van Leeuwen, T, & Lucka, F. (2021). Deep learning for multi-view ultrasonic image fusion. In IEEE International Ultrasonics Symposium. doi:10.1109/IUS52206.2021.9593507
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Ganguly, P.S, Pelt, D.M, Gürsoy, D, De Carlo, F, & Batenburg, K.J. (2021). Improving reproducibility in synchrotron tomography using implementation-adapted filters. In Journal of Synchrotron Radiation (Vol. 28, pp. 1583–1597). doi:10.1107/S1600577521007153
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Domínguez-Delmás, M, Bossema, F.G, Dorscheid, J, Coban, S.B, Hall-Aquitania, M, Batenburg, K.J, & Hermens, E. (2021). X-ray computed tomography for non-invasive dendrochronology reveals a concealed double panelling on a painting from Rubens’ studio. PLoS ONE, 16(8). doi:10.1371/journal.pone.0255792
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Pan, B, Arridge, S, Lucka, F, Cox, B.T, Huynh, N, Beard, P, … Betcke, M. (2021). Photoacoustic reconstruction using sparsity in Curvelet frame: Image versus data domain. IEEE Transactions on Computational Imaging, 7, 879–893. doi:10.1109/TCI.2021.3103606
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Minnema, J, van Eijnatten, M.A.J.M, Der Sarkissian, H.H, Doyle, S, Koivisto, J, Wolff, J, … Batenburg, K.J. (2021). Efficient high cone-angle artifact reduction in circular cone-beam CT using deep learning with geometry-aware dimension reduction. Physics in Medicine and Biology, 66(13). doi:10.1088/1361-6560/ac09a1
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Andriiashen, V, & Kozhevnikov, D. (2021). Development of the projection-based material decomposition algorithm for Multienergy CT. IEEE Transactions on Radiation and Plasma Medical Sciences, 5(4), 517–527. doi:10.1109/TRPMS.2020.3022479
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Andriiashen, V, van Liere, R, van Leeuwen, T, & Batenburg, K.J. (2021). Unsupervised foreign object detection based on dual-energy absorptiometry in the food industry. Journal of Imaging, 7(7). doi:10.3390/jimaging7070104
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van Leeuwen, T, & Aravkin, A.Y. (2021). Variable projection for non-smooth problems. SIAM Journal on Scientific Computing, S249–S268. doi:10.1137/20M1348650
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Bossema, F.G. (2021). Three line trajectory X-ray tomography datasets of a small wooden block. doi:10.5281/zenodo.4541555
Software
ASTRA Toolbox: Commercial-class software for tomography imaging
The ASTRA Toolbox is a MATLAB and Python platform providing scalable, high-performance GPU primitives for 2D and 3D tomography, including building blocks for advanced reconstruction algorithms.
RECAST3D: a real-time visualization platform for tomographic imaging
RECAST3D provides real-time tomographic reconstruction and visualization of arbitrarily oriented 2D slices in a 3D volume.
Current projects with external funding
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Mathematics and Algorithms for 3D Imaging of Dynamic Processes ()
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Non-destructive 3D spectral imaging: applications in the poultry industry ()
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Cambridge RG99590 AIO cancer imaging optimisation (Cancer Imaging Optimisation)
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Cervical Disease Maps
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the Center for Optimal, Real-Time Machine Studies of the Explosive Universe (CORTEX)
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CT for Art: from Images to Patterns (IMPACT4Art)
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MUltiscale, Multimodal and Multidimensional imaging for EngineeRING (MUMMERING)
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Translation-Driven Development of Deep Learning for Simultaneous Tomographic Image Reconstruction and Segmentation (None)
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Deep learning and compressed sensing for ultrasonic nondestructive testin (PPS Applus RTD)
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Universal Three-dimensiOnal Passport for process Individualization in Agriculture (UTOPIA)
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Enabling X-ray CT based Industry 4.0 process chains by training Next Generation research experts (xCTing)
Related partners
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ABN AMRO Bank
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Fraunhofer Gesellschaft
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IBM
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Katholieke Universiteit Nijmegen
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Naturalis
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Netherlands eScience Center
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NIKHEF
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Rijksmuseum Amsterdam
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Universiteit Wageningen
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University of Cambridge
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Nederland Instituut voor Radio Astronomie
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GREEFA
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Katholieke Universiteit Leuven
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Rheinisch-Westfaelische Technische Hochschule Aachen
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SURFsara B.V.
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Universiteit Utrecht
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Universiteit van Amsterdam