Neural networks in image processing
This course covers typical applications of neural networks in image processing. As an introduction, algorithms for various image processing disciplines are taught, and their implementation in the field of neural networks is explained. Particular attention is given to the fundamentals of neural networks and their implementation in MATLAB.
The focus of the course is a supervised computer lab, in which students work on classic image processing tasks using neural networks and apply metrological analysis tools. Possible applications of the image processing methods learned are discussed using examples from medicine and telecommunications.
Computer Science degree program
Neural Networks in Image Processing is part of the minor “Computational Laser Metrology,” module INF-D-510 Fundamentals of the Minor (Lect./T/P: 0/2/0)
Electrical Engineering/Mechatronics/Teacher Education Degree Program
The three experiments can be completed in lieu of the practical experiments in the MT internship as part of the “Measurement Technology” module (Lect./T/P: 0/0/1)
Degree Program in Electrical Engineering/Mechatronics/Information Systems Engineering/Physics
The NNBV practicum is part of the “Measurement and Sensor Technology” module (L/T/P: 1/0/0).
You can also deepen your knowledge through a thesis, research project, advanced seminar, or as a student research assistant at our department. You can find additional opportunities in this overview.
This course is open to students majoring in Computer Science, Electrical Engineering, Mechatronics, Information Systems Engineering, Teacher Education, and Physics. Prior knowledge of mathematics (integral calculus, Fourier transform, statistics) is expected.
Please register for the course in OPAL to participate.
We currently plan to hold the course and the computer lab sessions in person.
If you have any questions about the course, please contact the organizer, Tom Glosemeyer
Winter Semester 2026/27
When & Where: Friday, 3rd lecture period (11:10 a.m. – 12:40 p.m.) BAR/I88
Introductory Session: October 16, 2026
Content:
1. Introduction to Digital Image Processing and Neural Networks 2. Experiment 1: Digit Classification 3. Experiment 2: Digit Reconstruction Through a Scattering Medium 4. Experiment 3: Mode Decomposition Using a Multimode Fiber
| Instructor | Organization | |
| Prof. Dr.-Ing. habil. Jürgen Czarske | Dipl.-Ing. Tom Glosemeyer |