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. Sparse solutions of linear systems
2. Sparse models
3. Measuring matrices
4. Reconstruction of a sparse signal
5. Property of restricted isometry
6. Coherence
7. Stability and robustness of the reconstruction
8. Algorithms for signal reconstruction
9. Random matrices and the bounded isometry property
10. Reconstruction of low-rank matrices
11. Applications in image processing

1. Compressive Imaging, B. Adcock, A. C. Hansen, Cambridge University Press, 2021.
2. A mathematical introduction to compressive sensing, S. Foucart, H. Rauhut, Birkhaauser, 2013.
3. Sparse modeling, I. Rish, G. Ya. Grabarnik, CRC, 2015.
4. Compressed sensing: Theory and applications, Y. C. Eldar, G. Kutyiniok (ur.), Cambridge University Press, 2012.

Ostali izborni predmeti - Regular study - Computer Science and Mathematics
Vezani kolegiji C - Regular study - Computer Science and Mathematics
Vezani kolegiji D - Regular study - Computer Science and Mathematics

Ostali izborni predmeti - Regular study - Computer Science and Mathematics
Vezani kolegiji C - Regular study - Computer Science and Mathematics
Vezani kolegiji D - Regular study - Computer Science and Mathematics