Overview

Welcome to the web pages of the lecture "Image Processing" in Summer Semester 2025. Here, you will find all content, in particular lecture slides and videos for the different chapters and topics. Additional material or links for further study will also be provided here.

Please carefully study the following detailed information on how the lecture will proceed and what will be required for the final examination.

Overall plan of the lecture

We will have alternating weeks with one lectures and two lectures each. In the weeks with only one lecture, there will be a bi-weekly exercise group in the remaining lecture slot. This exercise group will be led by my Ph.D. student Ulas Bingoel, who will guide you through the exercise sheets, which are designed to give you some practical implementation knowledge on the key methodology. We use MATLAB as a study language here. Solutions should be prepared, but do not have to be submitted, as we do not have the capacity to correct them. You have to be your own judge of your performance here. Note that you will not understand content by just listening to it, but only by working with the material yourself. Therefore, it is in your own interest to do the exercises, in particular, it is also the best preparation for the final exam.

There are accompanying lecture videos recorded in 2021 with the same content, use the schedule below to get a rough idea in which weeks we do the content of the respective videos. Exercises are designed to fit within this schedule.

Schedule for online sessions and content

The following is a preliminary schedule how the content of the lecture is distributed over the upcoming weeks. All videos and slides are already available for study if you want to get a preview.

In the table below, you find

1. Week: 7.-11. April

09.04. Lecture: IP 00 - Introduction
Content: Introduction to image processing, organization of the lecture
11.04. Lecture: IP 01 - Image Filtering Part 1, 2
Content: Images and noise, the correlation

2. Week: 14.-18. April

16.04. Lecture: IP 01 - Image Filtering Part 2,3
Content: Convolution and non-linear filters

3. Week: 21.-25. April

23.04. Exercise group: Introduction to Matlab
Prepare: Exercise sheet 1
25.04. Lecture: IP 02 - Image Features Part 1,2
Content: Image edges and image derivatives

4. Week: 28. April - 02. May

30.04. Lecture: IP 02 - Image Features Part 3
Content: Technical background: Singular Value Decomposition and Applications
02.05. Exercise group: Image filtering, image edges
Prepare: Exercise sheet 2

5. Week: 05. - 09. May

07.05. Lecture: IP 02 - Image Features Part 4
Content: Technical background: Principal Component Analysis
The structure tensor of an image
09.05. Lecture: IP 02 - Image Features Part 5
Content: Pattern Matching and Autocorrelation

6. Week: 12. - 16. May

14.05. Lecture: IP 03 - Local Models for Optic Flow
Content: Introduction to Optical Flow
16.05. Exercise group: SVD, The structure tensor of an image
Prepare: Exercise Sheet 3

7. Week: 19. May - 23. May

21.5. Lecture: IP 04 - Tracking
Content: Basics of tracking, the KLT tracker
23.5. Lecture: IP 05 - Frequency and Scale
Content: The Fourier transform of an image, Part 1 and 2a

8. Week: 26. May - 30. May

28.05. Lecture: IP 05 - Frequency and Scale
Content: The Fourier transform of an image, Part 2b and 3
30.05. Exercise group: Local Models for Optical Flow
Prepare: Exercise Sheet 4

9. Week: 2. June - 6. June

04.06. Lecture: IP 05 - Frequency and Scale
Content: The Fourier transform of an image, Part 4
06.06. Lecture: IP 06 - SIFT
Content: The scale-invariant feature transform

10. Week: 9. June - 13. June

11.06. Lecture IP 07 - Variational Optical Flow Part 1, 2
Content: Crash course on variational methods
13.06. Exercise group: The Fourier transform and filtering
Prepare: Exercise Sheet 5

11. Week: 16. - 20. June

Pentecost break

12. Week: 23. - 27. June

23.06. Lecture: IP 07 - Variational Optical Flow Part 2-4
Content: Horn-Schunck and modern algorithms
27.06. Exercise group: Scale space and SIFT
Prepare: Exercise Sheet 6

13. Week: 30. June - 04. July

02.07. Lecture IP 08 - Variational Inverse Problems Part 1,2
Content: Deconvolution and regularization of solutions
04.07. Lecture IP 08 - Variational Inverse Problems Part 3
Content: Variational inverse problems

14. Week: 07. - 11. July

09.07. Lecture IP 08 - Variational Inverse Problems Part 4
Content: Outlook: advanced variational methods and connections to deep learning
Exercise group: Variational methods
Prepare: Exercise Sheet 7

15. Week: 14. - 18. July

16.07. Question and answer session in preparation for the exam
18.07. Exam

Requirements for acceptance to the final exam

You should work on the exercises and are encouraged to submit solutions bi-weekly on Ilias, although we will not be able to check all of them. It is not a requirement for admission to the exam.


Final exam

Details about the final exam will be published on Ilias once we get closer to the date. A preview of the content is already uploaded on Ilias.