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
IMPACT4ART: the interplay between scientists, art historians, conservators and the general public
The wooden sculpture shown in the green oval is surrounded by mystery: How old is this wooden sculpture? Where and by whom was it made? Mathematics and computer science can help to answer such questions.
Insight in Wood: dating wooden objects with 3D CT imaging
Together with the Rijksmuseum, researchers of CIW's Computational Imaging group investigate art objects with the help of our state of the art FleX-ray scanner. PhD student Francien Bossema explains how they proceed.
Best Poster Prize for Allard Hendriksen
Allard Hendriksen, researcher at CWI’s Computational Imaging group was awarded the Best Poster Prize at the “Mathematics of Machine Learning” symposium.
EU Horizon 2020 grant award for realtime CT imaging project ‘xCting’
Project ‘xCTing’ from CWI’s Computational Imaging (CI) group and partners, has been awarded a grant from the EU Horizon 2020 Marie Curie Innovative Training Networks programme.
Current events
TECHNICAL ART HISTORY SERIES - Digital imaging methods for Cultural Heritage
- 2021-06-22T16:00:00+02:00
- 2021-06-22T17:30:00+02:00
TECHNICAL ART HISTORY SERIES - Digital imaging methods for Cultural Heritage
Start: 2021-06-22 16:00:00+02:00 End: 2021-06-22 17:30:00+02:00
The seminars are part of the Technical Art History Series in Advanced Imaging Technologies for Cultural Heritage organized by the Rijksmuseum, the Computational Imaging group at CWI Amsterdam, and the Venice Centre for Digital and Public Humanities.
Please join us on Tuesday June 22 (4-5.30 pm CEST). Please register following the link or QR code on the poster.
Title: Point cloud to Sound cloud: Digital Innovation and Historic Sound at Linlithgow Palace
Speakers: Sophia Mirashrafi (Historic Environment Scotland), James Cook (Edinburgh College of Art, Scotland)
If you missed one of the earlier events, here are the YouTube links.
1. First session ‘Unlocking Art History: Letterlocking, X-ray Microtomography, and the Virtual Unfolding Algorithm’
2. Second session ‘Artificial intelligence in art conservation and preservation: Experience from the Ghent Altarpiece’ and ‘Macro Scale Chemical Imaging on works by Van Eyck’
A blogpost on the first session can be found here.
A blogpost on the second session can be found here.
We will write a summarizing blogpost for each session, so please stay tuned!
Members
Associated Members
Publications
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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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Javaherian, A, Lucka, F, & Cox, B.T. (2020). Refraction-corrected ray-based inversion for three-dimensional ultrasound tomography of the breast. Inverse Problems, 36(12). doi:10.1088/1361-6420/abc0fc
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Viganò, N.R, Lucka, F, de la Rochefoucauld, O, Coban, S.B, van Liere, R, Fajardo, M, … Batenburg, K.J. (2020). Emulation of X-ray light-field cameras. Journal of Imaging, 6(12). doi:10.3390/jimaging6120138
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Zeegers, M.T, Pelt, D.M, van Leeuwen, T, van Liere, R, & Batenburg, K.J. (2020). Task-driven learned hyperspectral data reduction using end-to-end supervised deep learning. Journal of Imaging, 6(12). doi:10.3390/jimaging6120132
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Lagerwerf, M.J, Hendriksen, A.A, Buurlage, J, & Batenburg, K.J. (2020). Noise2Filter: fast, self-supervised learning and real-time reconstruction for 3D computed tomography. Machine Learning: Science and Technology, 2. doi:10.1088/2632-2153/abbd4d
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Serrano, A, Meijer, S, van Rijn, R.R, Coban, S.B, Reissland, B, Hermens, E, … van Bommel, M. (2020). A non-invasive imaging approach for improved assessments on the construction and the condition of historical knotted-pile carpets. Journal of Cultural Heritage. doi:10.1016/j.culher.2020.09.012
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Pilikos, G, Horchens, L, Batenburg, K.J, van Leeuwen, T, & Lucka, F. (2020). Fast ultrasonic imaging using end-to-end deep learning. In 2020 IEEE International Ultrasonics Symposium. doi:10.1109/IUS46767.2020.9251550
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Flenner, S, Storm, M, Kubec, A, Longo, E, Doring, F, Pelt, D.M, … Greving, I. (2020). Pushing the temporal resolution in absorption and Zernike phase contrast nanotomography: Enabling fast in situ experiments. Journal of Synchrotron Radiation, 27, 1339–1346. doi:10.1107/S1600577520007407
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Rens, E.G, Zeegers, M.T, Rabbers, I, Szabó, A, & Merks, R.M.H. (2020). Autocrine inhibition of cell motility can drive epithelial branching morphogenesis in the absence of growth. Philosophical Transactions of the Royal Society B, 375(1807). doi:10.1098/rstb.2019.0386
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Bossema, F.G. (2020). Line trajectory X-ray tomography for dendrochronology of large wooden objects.
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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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