Basic Information
Topics# Uniqueness of Sparse Solutions, Pursuit Algorithms,performance Of Algorithms - Equivalence Theorems, Handling Noisy Measurements - Uniqueness, Stability, Algorithms and Performance, Iterative Shrinkage Methods, Average Performance Analysis - Basics and Analysis of The Thresholding Algorithms, The Dantzig-selector Algorithm, Signal Processing With The Sparse-land Model, Handling Various Applications With Sparsity Estimation Point of View to Sparse Approximation, Approximation, Dictionary Learning (mod and K-svd), Image Compression, Image Denoising Algorithms, Compressed-sensing. Learning Outcomes# By The End of The Course The Student Will# 1. Understand The Role and Importance of Models in Various Signal And Image Processing Tasks. 2. Be Acquainted With The Sparsity-based Model, Both Theoretically and Algorithmically. 3. Be Able to Solve New Problems in Image Processing While Using This Model.
Faculty: Computer Science
|Undergraduate Studies
|Graduate Studies
Pre-required courses
46200 - Image Processing and Analysis or (234125 - Numerical Algorithms and 236200 - Signal) or (234125 - Numerical Algorithms and 236201 - Introduction to Data Processing And)