Mathematics of Signal and Image Processing
Welcome to the lecture Mathematical Signal and Image Processing
by Prof. Dr. Gitta Kutyniok.
Course Description
Signals and images are the most widely used types of data with numerous applications. The area of mathematical signal and image processing aims to develop methods for their acquisition and analysis, which arise from and allow a deep mathematical understanding. Of particular interest in this area are also inverse problems, which aim to recover signals or images from measurements in the sense of inverting the measurement operator. Intriguingly, various of the methods which were developed to solve such problems led in turn to deep mathematical theories, such as wavelet systems in the realm of approximation theory and functional analysis. Finally, a recent development also connects the area of mathematical signal and image processing with artificial intelligence, in particular, deep neural networks.
This lecture will provide an introduction into this exciting and rich research area. Some of the topics, which we will discuss, are:
- Compressed Sensing
- Frame Theory
- Inverse Problems
- Wavelet and Shearlet Theory
- Deep Neural Network
Schedule and Venue
Lecture (Prof. Dr. Gitta Kutyniok): Mo 14-16 + Th 10-12 (Room A 027)
Exercise Class (Adalbert Fono): TBA
Office Hours: TBA; please write a short e-mail that you plan to come.
Exam: TBA
Requirements
This course is targeted at Bachelor and Master students from mathematics, business mathematics, or financial mathematics, but also interested students from computer science and statistics are welcome. The course requires knowledge of the topics from the course “Maßtheorie und Integralrechnung mehrerer Variablen” and “Stochastics”. Moreover, basics of functional analysis are helpful, but not required
Registration
We ask all participants to enroll in the course in moodle: Mathematics of Signal and Image Processing (Kutyniok, Fono), enrollment key is MSIP24.