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
Veni grant for Daniël Pelt
The Netherlands Organisation for Scientific Research (NWO) has awarded a Veni grant to CWI researcher Daniël Pelt.
Computational Imaging group and Rijksmuseum project awarded with NICAS funding
The Netherlands Institute for Conservation, Art and Science (NICAS) has announced which proposals have been awarded in the call "An Integrated Approach to the Scientific Study and Conservation of Art", organised by NWO.
Joost Batenburg awarded with Dutch Award for ICT research 2018
Joost Batenburg has been awarded with the Dutch Award for ICT research 2018. CWI’s group leader Computational Imaging and professor at Leiden University receives the award for his research in the area of new algorithms for tomography. An amount of 50.000 euro is attached to the award. This amount can be freely spent, in order to reinforce Batenburg’s research.
CWI and Meyn Food Processing start joint research project on non-destructive 3D spectral Imaging
CWI’s Computational Imaging group and Meyn Food Processing will collaborate in a joint research project with the goal of developing novel 3D spectral imaging techniques that will be applied in the poultry industry. The research will be executed by using the technology made available in CWI's ’FleX-ray’ lab.
Members
Associated Members
Publications
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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.
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Buurlage, J. (2020, July). Real-time tomographic reconstruction.
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