1. komponenta

Lecture type	Total
Lectures	45

* Load is given in academic hour (1 academic hour = 45 minutes)

COURSE AIMS AND OBJECTIVES: The objective of the course is to acquaint students with the basic methods of sparse signal processing, from sparse sampling to compressed sensing, and the associated numerical algorithms, with an emphasis on application in image processing.

COURSE DESCRIPTION AND SYLLABUS:
1. Introduction and motivation; images as mathematical objects
2. Filters
3. Computerized tomography; wavefield imaging
4. Discrete Fourier transformation
5. Discrete wavelet transformation
6. Magnetic resonance imaging
7. Compressed sensing: analytical and probabilistic aspects
8. Algorithms for compressed sensing
9. Application of compressed sensing: processing of biomedical signals
10. Application of compressed sensing: reconstruction of images

1. Compressive Imaging, B. Adcock, A. C. Hansen, Cambridge University Press, 2021.
2. The mathematics of Signal Processing, S. B. Damelin, W. Miller, Jr, Cambridge University Press, 2012.
3. Sparse modeling, Rish, G. Ya. Grabarnik, CRC, 2015.
4. A mathematical introduction to compressive sensing, S. Foucart, H. Rauhut, Birkhäuser, 2013.

Izborni modul Obrada signala i strojni vid - Regular study - Applied Mathematics

Izborni modul Obrada signala i strojni vid - Regular study - Applied Mathematics

Izborni modul Obrada signala i strojni vid - Regular study - Applied Mathematics

Izborni modul Obrada signala i strojni vid - Regular study - Applied Mathematics