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
Postdoc position in computational imaging for industrial quality control
Centrum Wiskunde & Informatica (CWI) has a vacancy in the Computational Imaging research group for a three-year position, on the subject of Real-time analysis of volumetric data acquired from X-Ray imaging.
News
HISTORISCH BEWIJS: IN WELKE KIST VLUCHTTE HUGO DE GROOT UIT SLOT LOEVESTEIN?
In het AVROTROS TV programma 'Historisch Bewijs' werd onderzocht in welke kist Hugo de Groot uit zijn gevangenschap ontsnapte. CT technieken van de Computational Imaging groep van Joost Batenburg hielpen bij het vinden van antwoord op deze vraag.
CWI starts research project within NWO programme Smart Solutions for Horti- and Agriculture
A consortium led by Joost Batenburg of CWI’s Computational Imaging group has been awarded a grant of EUR 800.000 by the board of the NWO domain science.
EU grant for CWI and UCL to improve X-Ray scanning at lower doses with AI
CWI and UCL received an EU ATTRACT grant to deploy and develop AI techniques to improve X-ray CT scanning in such a way that much lower doses of radiation can be used to obtain high-resolution images. The project started in July 2019.
CWI part of NWA CORTEX consortium
The National Science Agenda has awarded a 5 million euro grant to CORTEX – the Center for Optimal, Real-Time Machine Studies of the Explosive Universe. Self-learning machines will hunt for explosions in the universe and speed up innovations in industry and society.
Members
- Vladyslav Andriiashen
- Joost Batenburg
- Francien Bossema
- Sophia Coban
- Viviane Desgrange
- Shannon Doyle
- Poulami Ganguly
- Adriaan Graas
- Allard Hendriksen
- Ajinkya Kadu
- Maximilian Kiss
- Tristan van Leeuwen
- Robert van Liere
- Felix Lucka
- Willem Jan Palenstijn
- Georgios Pilikos
- Richard Schoonhoven
- Dirk Schut
- Dzemila Sero
- Mathé Zeegers
Associated Members
Publications
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Kadu, A.A, Mansour, H, & Boufounos, P.T. (2020). High-contrast reflection tomography with total-variation constraints. IEEE Transactions on Computational Imaging, 6, 1523–1536. doi:10.1109/TCI.2020.3038171
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Schoonhoven, R.A, Buurlage, J, Pelt, D.M, & Batenburg, K.J. (2020). Real-time segmentation for tomographic imaging. In IEEE 30th International Workshop on Machine Learning for Signal Processing (pp. 1–6). doi:10.1109/MLSP49062.2020.9231642
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Pilikos, G, Horchens, L, Batenburg, K.J, van Leeuwen, T, & Lucka, F. (2020). Deep data compression for approximate ultrasonic image formation. In 2020 IEEE International Ultrasonics Symposium. doi:10.1109/IUS46767.2020.9251753
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Ganguly, P.S, Lucka, F, Hupkes, H.J, & Batenburg, K.J. (2020). Atomic super-resolution tomography. In Lecture Notes in Computer Science. doi:10.1007/978-3-030-51002-2_4
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Sero, D, Hermens, E, Garachon, I, Scholten, F, van Liere, R, & Batenburg, K.J. (2020). The study of three-dimensional fingerprints in cultural heritage: Current trends and challenges.
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Vanrompay, H, Buurlage, J, Pelt, D.M, Kumar, V, Zhuo, X, Liz-Marzán, L.M, … Batenburg, K.J. (2020). Real-Time Reconstruction of Arbitrary Slices for Quantitative and In Situ 3D Characterization of Nanoparticles. Particle and Particle Systems Characterization, 37(7). doi:10.1002/ppsc.202000073
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Bals, S, Albrecht, W, Vanrompay, H, Bladt, E, Skorikov, A, De Oliveira, T.M, … van Aert, S. (2020). Novel approaches for electron tomography to investigate the structure and stability of nanomaterials in 3 dimensions. Microscopy and Microanalysis. doi:10.1017/S1431927620017031
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Hendriksen, A.A, Pelt, D.M, & Batenburg, K.J. (2020). Noise2Inverse: Self-Supervised Deep Convolutional Denoising for Tomography. IEEE Transactions on Computational Imaging, 6, 1320–1335. doi:10.1109/TCI.2020.3019647
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Buurlage, J, Marone, F, Pelt, D.M, Palenstijn, W.J, Stampanoni, M, Batenburg, K.J, & Schlepütz, C.M. (2019). Real-time reconstruction and visualisation towards dynamic feedback control during time-resolved tomography experiments at TOMCAT. Nature Scientific Reports, 9(1). doi:10.1038/s41598-019-54647-4
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Der Sarkissian, H.H, Lucka, F, van Eijnatten, M.A.J.M, Colacicco, G, Coban, S.B, & Batenburg, K.J. (2019). A cone-beam X-ray computed tomography data collection designed for machine learning. Scientific Data, 6(1). doi:10.1038/s41597-019-0235-y
Software
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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Real-Time 3D Tomography ()
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Cervical Disease Maps
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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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Fraunhofer Gesellschaft
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Naturalis
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Rijksmuseum Amsterdam
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Universiteit Wageningen
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University of Cambridge
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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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Universiteit Utrecht