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.
News
Centrum Wiskunde & Informatica launches world-class 3D lab
On Thursday 18 May 2017 demissionary state secretary Sander Dekker opened a new research facility that belongs to the top of the world league at Centrum Wiskunde & Informatica. With the new scanner of this lab, it will be made possible for the first time to look inside objects in 3D during the scanning process and to adjust or zoom in while scanning.
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).
Members
Associated Members
Publications
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Buurlage, J. (2020, July). Real-time tomographic reconstruction.
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Kostenko, A, Palenstijn, W.J, Coban, S.B, Hendriksen, A.A, van Liere, R, & Batenburg, K.J. (2020). Prototyping X-ray tomographic reconstruction pipelines with FleXbox. SoftwareX, 11. doi:10.1016/j.softx.2019.100364
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McDonald, S.A, Coban, S.B, Sottos, N.R, & Withers, P.J. (2019). Tracking capsule activation and crack healing in a microcapsule-based self-healing polymer. Nature Scientific Reports, 9(1). doi:10.1038/s41598-019-54242-7
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Bender, H, Richard, O, Kundu, P, Favia, P, Zhong, Z, Palenstijn, W.J, … Schoenmakers, R. (2019). Combined STEM-EDS tomography of nanowire structures. Semiconductor Science and Technology, 34(11). doi:10.1088/1361-6641/ab4840
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Huynh, N, Lucka, F, Zhang, E, Betcke, M, Arridge, S, Beard, P, & Cox, B.T. (2019). Single-pixel camera photoacoustic tomography. Journal of Biomedical Optics, 24(12). doi:10.1117/1.JBO.24.12.121907
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Minnema, J, van Eijnatten, M.A.J.M, Hendriksen, A.A, Liberton, N.P.T.J, Pelt, D.M, Batenburg, K.J, … Wolff, J. (2019). Segmentation of dental cone-beam CT scans affected by metal artifacts using a mixed-scale dense convolutional neural network. Medical Physics. doi:10.1002/mp.13793
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Djurabekova, N, Goldberg, A, Hauptmann, A, Hawkes, D, Long, G, Lucka, F, & Betcke, M. (2019). Application of Proximal Alternating Linearized Minimization (PALM) and inertial PALM to dynamic 3D CT. In Proceedings of the 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine. doi:10.1117/12.2534827
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Rimpiläinen, V, Koulouri, A, Lucka, F, Kaipio, J., & Wolters, C.H. (2019). Improved EEG source localization with Bayesian uncertainty modelling of unknown skull conductivity. NeuroImage, 188, 252–260. doi:10.1016/j.neuroimage.2018.11.058
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Buurlage, J, Bisseling, R.H, & Batenburg, K.J. (2019). A geometric partitioning method for distributed tomographic reconstruction. Parallel Computing, 81, 104–121. doi:10.1016/j.parco.2018.12.007
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Zhong, Z. (2018, December 10). Reconstruction methods for combined HAADF-STEM and EDS tomography.
Software
Current projects with external funding
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Machine learning for large 3D tomographic images ()
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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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PPS contract Planmeca ()
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Real-Time 3D Tomography ()
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Cambridge G102101: HPV mediated cervical lesions
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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