EN CH

Wavelets and Sparse Signal Processing

Course name:Wavelets and Sparse Signal Processing

Course code:IE28007

Credit/class hours:2/32

 

Textbooks and references

(1)Stéphane Mallat, A Wavelet Tour to Signal Processing, The Sparse Way, Third Edition, Elsevier, 2009

(2)Michael Elad, Sparse and Redundant Representations, From Theory to Applications in Signal and Image Processing, Springer, 2010

(3)Alan V. Oppenheim, Signals & Systems, Second Edition, Publishing House of Electronics Industry of China

(4)http://www.ifp.illinois.edu/~minhdo/teaching/wavelets.html

(5)http://www-stat.stanford.edu/~wavelab

(6)https://elad.cs.technion.ac.il/236862-course-webpage-winter-semester-2018-2019/

 

Course Details

This course aims at presenting the basic theory of wavelet transform-based multimedia signal processing to graduate students. Starting from the basic idea of time-frequency analysis, the course presents the basic definition and property of wavelet transform, filter bank and multi-scale geometry analysis, sparse representation, and other signal processing methods. The course also provides multimedia signal processing tools developed recently based on the idea of wavelet, such as wavelet scattering transform and graph wavelet. It also covers the application method and latest developments of signal processing approaches based on wavelet analysis, fractal and sparse representation in aspect of image and video signal processing.

 

Syllabus

Chapter Topic Lecture Note Remark
0 Introduction Lecture0  
1 Fundamentals Lecture01020304  
2 Time Meets Frequency Lecture05  
3 Frames Lecture06  
4 Wavelet Bases Lecture07  
5 Wavelet Zoom Lecture08  
6 Multiscale Geometry Analysis Lecture09  
7 Lifting Wavelet and Filterbank Lecture10  

 

Requirements and grading

Homework and attendance: 20% Homework should be written in ENGLISH.

Projects: 40% Requirements

Final Exam: 40% Requirements Exam_Paper_update

 

Contact

fw.key@sjtu.edu.cn (TA, Wen Fei)

lumiran@outlook.com (TA, Tianran Wu)

沪交ICP备20160059

Institute of Media, Information, and Network (MIN Lab)