 2016 - 2017 | |||||||||||||||||||||||||||||||||||
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| 0510-7250-01 | Sparse Representations and Their Applications in Signal and Image Processing | ||||||||||||||||||||||||||||||||||
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| FACULTY OF ENGINEERING | |||||||||||||||||||||||||||||||||||
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University credit hours: 2.0
Course description
Uniqueness of sparse representations, pursuit algorithms, Performance and stability, iterative shrinkage methods, average case performance analysis, sparsity models based signal processing, variety of related applications, the Bayesian approach for the sparsity model, dictionary learning techniques (MOD and KSVD), the denoising problem, compressed sensing.