Welcome to the homepage of the lecture

Differential Equations in Image Processing and Computer Vision

Winter Term 2026 / 2027

Differential Equations in Image Processing and Computer Vision

Five Teaching Awards (4 in Computer Science, 1 in Mathematics)

Lecturer: Prof. Joachim Weickert

Assistant: Vassillen Chizhov

Winter Term 2026 / 2027

Lectures (4h) with theoretical and programming assignments (2h);
(9 ETCS points)

Online Lectures:
Wednesday, 10:15-12:00
Friday, 10:15-12:00

First Lecture: Wednesday, October 14

In Person Tutorial:
Tuesday, 16:15-18:00, in E1.3, SR 016

First Tutorial: Tuesday, October 20

Many model-based techniques in image processing and computer vision make use of methods based on partial differential equations (PDEs) and variational calculus. Moreover, many classical methods may be reinterpreted as approximations of PDE-based techniques. In this course we will get an in-depth insight into these methods. For each of these techniques, we will discuss the basic ideas as well as theoretical and algorithmic aspects. Examples from medical imaging and other fields illustrate the various application possibilities. In this lecture we focus on classical, model-based image processing methods, not on deep learning. Since this class guides its participants to many research topics in our group, its attendance is required for everyone who wishes to pursue a master thesis in our group.

For registration and more detailed information, please visit the CMS.

MIA Group
©2001-2023
The author is not
responsible for
the content of
external pages.

Imprint - Data protection