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
No vacancies currently.
News
Insight in Wood: dating wooden objects with 3D CT imaging
Together with the Rijksmuseum, researchers of CIW's Computational Imaging group investigate art objects with the help of our state of the art FleX-ray scanner. PhD student Francien Bossema explains how they proceed.
Best Poster Prize for Allard Hendriksen
Allard Hendriksen, researcher at CWI’s Computational Imaging group was awarded the Best Poster Prize at the “Mathematics of Machine Learning” symposium.
EU Horizon 2020 grant award for realtime CT imaging project ‘xCting’
Project ‘xCTing’ from CWI’s Computational Imaging (CI) group and partners, has been awarded a grant from the EU Horizon 2020 Marie Curie Innovative Training Networks programme.
CWI develops New Techniques for Real-time Tomographic Reconstruction
Jan-Willem Buurlage of CWI's Computational Imaging group introduces various techniques that significantly reduce the time it takes to run conventional tomographic reconstruction algorithms without affecting image quality. He will defend his thesis at 1 July.
Members
- Vladyslav Andriiashen
- Joost Batenburg
- Francien Bossema
- Sophia Coban
- Viviane Desgrange
- Shannon Doyle
- Poulami Ganguly
- Adriaan Graas
- Allard Hendriksen
- Ajinkya Kadu
- Maximilian Kiss
- Tristan van Leeuwen
- Robert van Liere
- Felix Lucka
- Willem Jan Palenstijn
- Georgios Pilikos
- Richard Schoonhoven
- Dirk Schut
- Dzemila Sero
- Mathé Zeegers
Associated Members
Publications
-
Kadu, A.A, Mansour, H, & Boufounos, P.T. (2020). High-contrast reflection tomography with total-variation constraints. IEEE Transactions on Computational Imaging, 6, 1523–1536. doi:10.1109/TCI.2020.3038171
-
Schoonhoven, R.A, Buurlage, J, Pelt, D.M, & Batenburg, K.J. (2020). Real-time segmentation for tomographic imaging. In IEEE 30th International Workshop on Machine Learning for Signal Processing (pp. 1–6). doi:10.1109/MLSP49062.2020.9231642
-
Pilikos, G, Horchens, L, Batenburg, K.J, van Leeuwen, T, & Lucka, F. (2020). Deep data compression for approximate ultrasonic image formation. In 2020 IEEE International Ultrasonics Symposium. doi:10.1109/IUS46767.2020.9251753
-
Ganguly, P.S, Lucka, F, Hupkes, H.J, & Batenburg, K.J. (2020). Atomic super-resolution tomography. In Lecture Notes in Computer Science. doi:10.1007/978-3-030-51002-2_4
-
Sero, D, Hermens, E, Garachon, I, Scholten, F, van Liere, R, & Batenburg, K.J. (2020). The study of three-dimensional fingerprints in cultural heritage: Current trends and challenges.
-
Vanrompay, H, Buurlage, J, Pelt, D.M, Kumar, V, Zhuo, X, Liz-Marzán, L.M, … Batenburg, K.J. (2020). Real-Time Reconstruction of Arbitrary Slices for Quantitative and In Situ 3D Characterization of Nanoparticles. Particle and Particle Systems Characterization, 37(7). doi:10.1002/ppsc.202000073
-
Bals, S, Albrecht, W, Vanrompay, H, Bladt, E, Skorikov, A, De Oliveira, T.M, … van Aert, S. (2020). Novel approaches for electron tomography to investigate the structure and stability of nanomaterials in 3 dimensions. Microscopy and Microanalysis. doi:10.1017/S1431927620017031
-
Hendriksen, A.A, Pelt, D.M, & Batenburg, K.J. (2020). Noise2Inverse: Self-Supervised Deep Convolutional Denoising for Tomography. IEEE Transactions on Computational Imaging, 6, 1320–1335. doi:10.1109/TCI.2020.3019647
-
Buurlage, J, Marone, F, Pelt, D.M, Palenstijn, W.J, Stampanoni, M, Batenburg, K.J, & Schlepütz, C.M. (2019). Real-time reconstruction and visualisation towards dynamic feedback control during time-resolved tomography experiments at TOMCAT. Nature Scientific Reports, 9(1). doi:10.1038/s41598-019-54647-4
-
Der Sarkissian, H.H, Lucka, F, van Eijnatten, M.A.J.M, Colacicco, G, Coban, S.B, & Batenburg, K.J. (2019). A cone-beam X-ray computed tomography data collection designed for machine learning. Scientific Data, 6(1). doi:10.1038/s41597-019-0235-y
Software
Current projects with external funding
-
Mathematics and Algorithms for 3D Imaging of Dynamic Processes ()
-
Non-destructive 3D spectral imaging: applications in the poultry industry ()
-
Real-Time 3D Tomography ()
-
Cervical Disease Maps
-
CT for Art: from Images to Patterns (IMPACT4Art)
-
MUltiscale, Multimodal and Multidimensional imaging for EngineeRING (MUMMERING)
-
Translation-Driven Development of Deep Learning for Simultaneous Tomographic Image Reconstruction and Segmentation (None)
-
Deep learning and compressed sensing for ultrasonic nondestructive testin (PPS Applus RTD)
-
Universal Three-dimensiOnal Passport for process Individualization in Agriculture (UTOPIA)
-
Enabling X-ray CT based Industry 4.0 process chains by training Next Generation research experts (xCTing)
Related partners
-
Fraunhofer Gesellschaft
-
Naturalis
-
Rijksmuseum Amsterdam
-
Universiteit Wageningen
-
University of Cambridge
-
GREEFA
-
Katholieke Universiteit Leuven
-
Rheinisch-Westfaelische Technische Hochschule Aachen
-
Universiteit Utrecht