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Differential Equations in Image Processing and Computer Vision

Winter Term 2025/2026

Differential Equations in Image Processing and Computer Vision

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

Lecturer: Prof. Joachim Weickert

Assistant: Daniel Gaa

Winter Term 2025/2026

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 15

In Person Tutorial:
Tuesday, 16:15-18:00, in E1.1, SR 206

First Tutorial: Tuesday, October 21

Exams:
First Exam: Friday, February 20, 14:00 - 17:00, in E1.3 HS002
Second Exam: Wednesday, March 25, 14:00 - 17:00, in E1.3 HS002

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.

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