Description
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
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.
Vici grant for Joost Batenburg
Researcher Joost Batenburg of CWI has been awarded a Vici grant from the Netherlands Organisation for Scientific Research (NWO) for his proposal 'Real-Time 3D Tomography'. With the 1.5 million euro grant, Batenburg can develop his own research line in the next five years.
CWI reduces perturbations in tomography
Researcher Folkert Bleichrodt of CWI has developed new methods to improve the applicability of tomography. These methods reduce unwanted perturbations. This allows for applying tomography on a very small scale or based on a small number of measurements.
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