Date |
Content of the Lecture |
Assignments/Readings/Notes |
Lecture 1: 4th Jan (Tue) |
- Course overview: intro to compressed sensing, tomography, dictionary and transform learning, low rank matrix recovery
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Lecture 2: 7th Jan (Fri) |
- Compressed sensing (CS): introduction and motivation
- Review of DFT and DCT, Review of JPEG, representation of a signal/image as a linear combination of basis vectors
- Sparsity of natural images in transform bases
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Lecture 3: 7th Jan (Fri) |
- Candes, Romberg, Tao: puzzling experiment; Basic optimization problem for CS involving the total variation (i.e. sum total gradient magnitude of the image)
- Concept of incoherence between sensing matrix and representation basis
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Lecture 4: 11th Jan (Tue) |
- Theorem 1 for reconstruction guarantees
- Whittaker-Shannon sampling theorem
- Comparison between Theorem 1 and Shannon's sampling theorem.
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Lecture 5: 11th Jan (Tue) |
- Theorem 1 for reconstruction guarantees
- Whittaker-Shannon sampling theorem
- Comparison between Theorem 1 and Shannon's sampling theorem.
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