E9 203-O Compressed Sensing and Sparse Signal Processing 3:1 (January 2023)
Course Instructor: K V S Hari, ECE
Syllabus
Introduction to underdetermined linear Systems; Sparse linear systems.Basic theory of l0 and l1 minimization and necessary and sufficient
conditions for l0 - l1 equivalence. Uncertainty of Redundant Solutions.
Spark of a matrix, Mutual coherence and the Restricted Isometry property (RIP).
Greedy pursuit algorithms for sparse signal recovery: Orthogonal Matching Pursuit (OMP), Basis Pursuit (BP), basis Pursuit De-Noising (BPDN),
Iteratively Reweighted Least Squares (IRLS) method, Stage-wise OMP (StOMP), Dantzig-Selector approach,
Dictionary Learning: Method of Optimal Directions (MOD), K-SVD algorithm
Application areas: Image processing: Image deblurring, Image compression, Image denoising examples, wireless communication: Channel estimation.
Textbooks / References
- M. Elad, "Sparse and Redundant Representations – From Theory to Applications in Signal and Image Processing", Springer, 2010.
- S. Foucart and H Rauhut, "A Mathematical Introduciton to Compressive Sensing", Birkhauser, 2010.
Prerequisites: Matrix Theory
Grading:
- Assignments 40%
- Midterm exam 30%
- Final exam 30%.
Assignments will be given roughly once in two weeks.