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
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).
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.
Members
Associated Members
Publications
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Sero, D, Zaidi, A, Li, J, White, J.D, González Zarzar, T.B.G, Marazita, M.L, … Claes, P. (2019). Facial recognition from DNA using face-to-DNA classifiers. Nature Communications, 10(1). doi:10.1038/s41467-019-10617-y
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Hendriksen, A.A, Pelt, D.M, Palenstijn, W.J, Coban, S.B, & Batenburg, K.J. (2019). On-the-fly machine learning for improving image resolution in tomography. Applied Sciences (Switzerland), 9(12). doi:10.3390/app9122445
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Bossema, F.G, Burger, J.P, Bratton, L, Challenger, A, Adams, R.C, Sumner, P, … Smeets, I. (2019). Expert quotes and exaggeration in health news: A retrospective quantitative content analysis. Wellcome Open Research. doi:10.12688/wellcomeopenres.15147.2
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Viganò, N.R, Martínez Gil, P.M, Herzog, C, de la Rochefoucauld, O, Liere, R.V. (Robert Van), & Batenburg, K.J. (2019). Advanced light-field refocusing through tomographic modeling of the photographed scene. Optics Express, 27(6), 7834–7856. doi:10.1364/OE.27.007834
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Pelt, D.M, Batenburg, K.J, & Sethian, J.A. (2018). Improving tomographic reconstruction from limited data using Mixed-Scale Dense convolutional neural networks. Journal of Imaging, 4(11), 128–128. doi:10.3390/jimaging4110128
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Tick, J, Pulkkinen, A, Lucka, F, Ellwood, R, Cox, B.T, Kaipio, J.P. (Jari), … Tarvainen, T. (2018). Three dimensional photoacoustic tomography in Bayesian framework. The Journal of the Acoustical Society of America, 144(4), 2061–2071. doi:10.1121/1.5057109
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Der Sarkissian, H.H, Viganò, N.R, & Batenburg, K.J. (2018). A data consistent variational segmentation approach suitable for real-time tomography. Fundamenta Informaticae, 163(1), 1–20. doi:10.3233/FI-2018-1729
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Hauptmann, A, Arridge, S, Lucka, F, Muthurangu, V, & Steeden, J.A. (2018). Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning–proof of concept in congenital heart disease. Magnetic Resonance in Medicine. doi:10.1002/mrm.27480
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Buurlage, J, Bannink, T.R, & Bisseling, R.H. (2018). Bulk: A modern C++ interface for bulk-synchronous parallel programs. In Euro-Par 2018: Parallel Processing (pp. 519–532). doi:10.1007/978-3-319-96983-1_37
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Treeby, B, Lucka, F, Martin, E, & Cox, B.T. (2018). Equivalent-source acoustic holography for projecting measured ultrasound fields through complex media. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. doi:10.1109/TUFFC.2018.2861895
Software
Current projects with external funding
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Machine learning for large 3D tomographic images ()
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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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Mathematics and Algorithms for 3D Imaging of Dynamic Processes
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PPS contract Planmeca
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CT for Art: from Images to Patterns (IMPACT4Art)
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Combining cycloidal computed tomography with machine learning: a mechanism to disrupt the costly relationship between spatial resolution and radiation dose (ML-CYCLO-CT)
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MUltiscale, Multimodal and Multidimensional imaging for EngineeRING (MUMMERING)
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Deep learning and compressed sensing for ultrasonic nondestructive testin (PPS Applus RTD)
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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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Naturalis
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Planmeca
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Rijksmuseum Amsterdam
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Universidad Politécnica de Madrid
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University College London