Kies de Nederlandse taal
Course module: 201900209
Inverse Problems in Imaging
Course info
Course module201900209
Credits (ECTS)6
Course typeCourse
Language of instructionEnglish
Contact personprof.dr. C. Brune
prof.dr. C. Brune
Contactperson for the course
prof.dr. C. Brune
Externe Docent
Academic year2022
Starting block
RemarksSemester course; runs over 2A and 2B. Mastermath course. Lecturers are Christoph Brune (UT) and Tristan van Leeuwen (UU)
Application procedureYou apply via OSIRIS Student
Registration using OSIRISYes
The main learning goal for the students is to model, analyze and use state-of-the-art variational methods, PDEs and optimization techniques to solve challenging inverse problems in imaging. Upon completing this course, students achieved the following learning goals:
  • Problem identification: Identification of imaging problems as mathematical inverse operator problems (e.g. integral equations, dynamical systems);
  • Modeling and discretization: Problem formulation arising in applications using the language of nonlinear variational methods and partial differential equations; use Bayesian modeling to take data and model uncertainty into account; continuous versus discrete modeling;
  • Analysis: Understanding the main concepts of nonlinear regularization theory in an analogous way in PDEs as well as in variational methods, and how it influences existence (duality, weak topologies, Theorem of Banach-Alaoglu) and uniqueness results;
  • Numerical optimization: Formulating optimality conditions (variations) for constrained convex variational methods (saddle point problems) and to solve them via primal-dual methods or discretized higher-order methods.
→   At the end of the course participants will be able to tackle inverse problems for imaging in biomedicine or geophysics with a new repertoire of state-of-the-art mathematical tools.
This course is about inverse problems in imaging. The mathematical reconstruction and processing of images is of fundamental importance in state-of-the-art applications in health and geosciences, e.g. in medical tomography, in high-resolution microscopy or in geophysical inversion. In many cases, underlying inverse problems can be formulated and solved using variational methods and partial differential equations. This course offers a theoretical as well as an applied insight into inverse problems and variational methods for mathematical imaging. It addresses reconstruction problems of different imaging modalities (e.g. CT or PET) in biomedicine and geophysics. The course covers the full chain of solving inverse problems in imaging, namely

Problem identification → Modeling and discretization → Analysis → Numerical optimization

where variational principles, regularization theory and numerical optimization (scientific computing) form the underlying joint core. The course connects and extends upon the main concepts of basic courses on differential equations and numerics.
Assumed previous knowledge
The course is aimed at Master and starting PhD students in Mathematics (and Technical Medicine) at the comprehensive as well as the technical universities. Solid knowledge of linear algebra and calculus are essential. Furthermore, knowledge of differential equations and numerics is desirable. All at the Bachelor level.
Participating study
Master Applied Mathematics
Required materials
Course material
Brune, van Leeuwen – Lecture Notes for Inverse Problems in Imaging (available in Mastermath and online
Recommended materials
Aubert, Kornprobst - Mathematical Problems in Image Processing, (Partial Differential Equations and the Calculus of Variations), Springer, 2nd edition, 2006 (available in UT network) ISBN: 978-0-387-44588-5
Scherzer, Grasmair, Grossauer, Haltmeier, Lenzen - Variational Methods in Imaging, Applied Mathematical Sciences 167, Springer, 2009 (available in UT network) ISBN: 978-0-387-69277-7
Burger, Osher - A Guide to the TV Zoo, Level Set and PDE Based Reconstruction Methods in Imaging, LNM, Springer, 2013 (available in UT network) ISBN: 978-3-319-01712-9
Instructional modes
Presence dutyYes

Presence dutyYes

Presence dutyYes

Project supervised
Presence dutyYes


Individual homework assignments (20%)
Oral exam (40%)
Final project (40%)

Kies de Nederlandse taal