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
Jeroen Hazewinkel wins prize for best presentation
At the annual meeting of the J.M. Burgers Centrum (the Research School for Fluid Dynamics) Jeroen Hazewinkel of the Centrum Wiskunde & Informatica (CWI) has been awarded the first prize for his presentation about tomographical (3D) reconstructions of internal water waves.
Better image quality for virtual reality with computer science
Less image delays, less 'image echoes' and more fluid movements in virtual reality (VR): this is all possible with the research of Ferdi Smit of the Centrum Wiskunde & Informatica (CWI), the Dutch national research center for mathematics and computer science in Amsterdam, the Netherlands.
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