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
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.
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.
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