Leader of the group Computational Imaging: Tristan van Leeuwen.


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.




No vacancies currently.


The Netherlands’ smallest supercomputer is here

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.

The Netherlands’ smallest supercomputer is here - Read More…

Current events

First annual meeting of the Dutch Inverse Problems Community

  • 2021-11-25T00:00:00+01:00
  • 2021-11-26T23:59:59+01:00
November 25 Thursday

Start: 2021-11-25 00:00:00+01:00 End: 2021-11-26 23:59:59+01:00

Conference center de Werelt, Lunteren

The first annual meeting of the Dutch Inverse Problems Community will take place on 25-26 November 2021 in conference center de Werelt, Lunteren.


The registration fee for this 2-day event is EUR 235, which includes hotel and catering. Registering for a single day only is possible as well and costs EUR 50, which includes lunch.

You can register here. Currently, the registration is limited to 25 people and is on a first-come-first-served basis. If registration is full, we will put you on a waiting list and contact you if extra spots become available.

 Preliminary program:

Thursday 25 November

09.30 - 10.00: Reception, coffee

10.00 - 12.30: Masterclasses 1 & 2 (in parallel)

12.30 - 14.00: Lunch break and poster session

14.00 - 16.30: Masterclasses 1 & 2 (in parallel)

16.30 - 17.30: Drinks and poster session

17:30 - 18:30: Brainstorm session on joint grant proposals etc.

18.30 -           : Dinner, followed by social program


Friday 26 November

09.00 - 09.30: Reception, coffee

09.30 - 10.30: Plenary talks

10.30 - 11.00: Coffee break

11.00 - 12.00: Plenary talks

12.00 - 13.30: Lunch break

13.30 - 14.30: Plenary talks

14:30 - 16:30: Panel discussion


The masterclasses are meant to introduce the participants to a particular topic by a combination of lecture and hands-on exercises. Each masterclass lasts to whole day (10:00 - 16:30 with lunch break), so you can register for one masterclass. During registration you will be asked to pick a top 3 from the topics below. As we can only offer two masterclasses, you may not be assigned your first pick.

Basic inverse problems and imaging theory - Martin van Gijzen This master class will discuss the theory of linear discrete inverse problems with application to tomographic image reconstruction.  Linear discrete inverse problems can be formulated as a least-squares problem. This least-squares problem is typically ill-posed, which means that it does not have a unique solution, or that the solution is very sensitive to noise. Consequently, we can not expect that a standard technique like solving an ill-posed least-squares problem by solving the normal equations will lead to a relevant solution. However, the solution of such problems are of pivotal importance in many applications. The class starts with explaining how tomographic image reconstruction yields an ill-posed least-squares problem. Then we review linear algebra techniques for solving such problems. We will introduce the concept of regularisation to make the problem well-posed, and discuss a number of ideas of how to regularise. Finally, we will present and study solution algorithms. The class finishes with an assignment in which the theory and algorithms are applied to a small but very illustrative tomographic reconstruction problem that models wave propagation in the earth crust. Entry requirements: Basic knowledge of linear algebra.

Optimisation techniques in inverse problems - Juan Peypouquet In this course, we will discuss how some optimisation techniques can be used to analyse and solve a class of inverse problems. The course will contain a self-contained review of convex analysis, subdifferential calculus and optimality conditions, plus an introduction to iterative methods used to solve these kinds of problems. Entry requirements: Bachelor-level knowledge of functional analysis and differential calculus is recommended

Data assimilation - Femke Vossepoel Data assimilation combines dynamic models with available observations to find the probability distribution of the model solution given the data. In the last decades, we see a growing application of data assimilation in the geosciences, but also in other fields, from economics to epidemiology. In this master class, we will explain the principles of data assimilation from a Bayesian perspective and provide a unified formulation of data assimilation that places various data assimilation methods and their applications in perspective. We will discuss how to use data assimilation for state and parameter estimation and we will discuss how these methods can deal with errors in the dynamic model and its control or forcing. Participants will experience the possibilities and limitations of data assimilation in an exercise with a toy problem. Entry requirements: MSc-level education in physics, maths, engineering or geosciences. Basic knowledge of inverse problem theory and Bayesian statistics.

Fundamentals of Acoustic and Electromagnetic Wave Field Theory - Koen van Dongen & Rob Remis  Inverse wave field problems play an important role in many applications, such as reservoir imaging, hyperthermia cancer treatment, MRI, medical ultrasound, etc. Mathematical data and wave field models play a fundamental role in such problems. The models should capture the underlying physical processes as accurately as possible, of course, and efficient inverse solution techniques often exploit the structure and properties of these models.  To effectively solve an inverse wave field problem, a proper understanding of the physical wave field processes is required. We therefore provide a crash course on acoustic and electromagnetic wave field theory. After this course, we hope that you have a better understanding of acoustic and electromagnetic wave propagation, Rayleigh integrals, Greens functions, scattering integrals, boundary conditions etc. Hopefully, this knowledge will help you to improve the mathematical model or solution method used to solve your inverse problem. Entry requirements: No specific requirements; basic knowledge of differential equations and/or physics is helpful.

PhD defense Rien Lagerwerf

  • 2021-10-05T10:00:00+02:00
  • 2021-10-05T11:00:00+02:00
October 5 Tuesday

Start: 2021-10-05 10:00:00+02:00 End: 2021-10-05 11:00:00+02:00

Everyone is invited to attend the public defense of Rien Lagerwerf of his PhD thesis:

Automatic and Efficient Tomographic Reconstruction algorithms

Promotor: Prof. dr. Joost Batenburg
Leiden Institute of Advanced Computer Science (LIACS), Universiteit Leiden

Co-promotor: Dr. Willem Jan Palenstijn
Centrum Wiskunde & Informatica


Associated Members



Current projects with external funding

  • Mathematics and Algorithms for 3D Imaging of Dynamic Processes ()
  • Non-destructive 3D spectral imaging: applications in the poultry industry ()
  • All in one cancer imaging optimisation using an integrated mathematical and deep learning (Cancer Imagiing)
  • Cervical Disease Maps
  • the Center for Optimal, Real-Time Machine Studies of the Explosive Universe (CORTEX)
  • 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

  • ABN AMRO Bank
  • Fraunhofer Gesellschaft
  • IBM
  • Katholieke Universiteit Nijmegen
  • Naturalis
  • Netherlands eScience Center
  • Rijksmuseum Amsterdam
  • Universiteit Wageningen
  • University of Cambridge
  • Nederland Instituut voor Radio Astronomie
  • Katholieke Universiteit Leuven
  • Rheinisch-Westfaelische Technische Hochschule Aachen
  • SURFsara B.V.
  • Universiteit Utrecht
  • Universiteit van Amsterdam