
The main learning goal for the students is to model, analyze and use stateoftheart 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 BanachAlaoglu) and uniqueness results;
 Numerical optimization: Formulating optimality conditions (variations) for constrained convex variational methods (saddle point problems) and to solve them via primaldual methods or discretized higherorder 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 stateoftheart mathematical tools.



This course is about inverse problems in imaging. The mathematical reconstruction and processing of images is of fundamental importance in stateoftheart applications in health and geosciences, e.g. in medical tomography, in highresolution 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 knowledgePrerequisites 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. 
Master Applied Mathematics 
  Required materialsCourse materialBrune, van Leeuwen – Lecture Notes for Inverse Problems in Imaging (available in Mastermath and online
https://tristanvanleeuwen.github.io/IP_and_Im_Lectures/intro.html 

 Recommended materialsBookAubert, Kornprobst  Mathematical Problems in Image Processing, (Partial Differential Equations and the Calculus of Variations), Springer, 2nd edition, 2006 (available in UT network)
ISBN: 9780387445885 
 BookScherzer, Grasmair, Grossauer, Haltmeier, Lenzen  Variational Methods in Imaging, Applied Mathematical Sciences 167, Springer, 2009 (available in UT network)
ISBN: 9780387692777 
 BookBurger, Osher  A Guide to the TV Zoo, Level Set and PDE Based Reconstruction Methods in Imaging, LNM, Springer, 2013 (available in UT network)
ISBN: 9783319017129 

 Instructional modesAssessmentPresence duty   Yes 
 AssignmentPresence duty   Yes 
 LecturePresence duty   Yes 
 Project supervisedPresence duty   Yes 

 TestsTest RemarkIndividual homework assignments (20%) Oral exam (40%) Final project (40%)


 