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
Postdoc on the subject of Pattern Recognition and Analysis for Technical Art History
Centrum Wiskunde & Informatica (CWI) has a vacancy in the Computational Imaging research group for a talented Postdoc on the subject of Pattern Recognition and Analysis for Technical Art History
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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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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van Liere, R. (2019). Flexibele CT scanner maakt interactieve 3D beelden - Bits & Chips- 8-1-2019.
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Buurlage, J.W, 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.
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Zeegers, M.T, Lucka, F, & Batenburg, K.J. (2018). A Multi-Channel DART algorithm. In Combinatorial Image Analysis (pp. 164–178). doi:10.1007/978-3-030-05288-1_13
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Tovey, R, Benning, M, Brune, C, Lagerwerf, M.J, Collins, S.M, Leary, R, … Schönlieb, C.-B. (2018). Directional sinogram inpainting for limited angle tomography. Inverse Problems. doi:10.1088/1361-6420/aaf2fe
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Zhong, Z, Palenstijn, W.J, Viganò, N.R, & Batenburg, K.J. (2018). Numerical methods for low-dose EDS tomography. Ultramicroscopy, 194, 133–142. doi:10.1016/j.ultramic.2018.08.003
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Kostenko, A, Andriiashen, V, & Batenburg, K.J. (2018). Registration-based multi-orientation tomography. Optics Express, 26(22), 28982–28995. doi:10.1364/OE.26.028982
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Lucka, F, Huynh, N, Betcke, M, Zhang, E, Beard, P, Cox, B.T, & Arridge, S. (2018). Enhancing compressed sensing 4D photoacoustic tomography by simultaneous motion estimation. SIAM Journal on Imaging Sciences, 11(4), 2224–2253. doi:10.1137/18M1170066
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Hauptmann, A, Cox, B.T, Lucka, F, Huynh, N, Betcke, M, Beard, P, & Arridge, S. (2018). Approximate k-space models and deep learning for fast photoacoustic reconstruction. In Machine Learning for Medical Image Reconstruction (pp. 103–111). doi:10.1007/978-3-030-00129-2_12
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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Rapid 3D Printing through full-field volumetric photolithography
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Real-Time 3D Tomography
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The See-Through Museum
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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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Volumetric medical x-ray imaging at extremely low dose (VOXEL)
Related partners
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Centre National de la Recherche Scientifique (CNRS), Gif-sur-Yvette, France
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CNR Pisa
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COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES
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IMAGINE OPTIC
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Meyn Food Processing Technology B.V.
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Naturalis
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Planmeca
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Rijksmuseum Amsterdam
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Universidad Politécnica de Madrid