- Lecture 1: The core sparse approximation problem and pursuit algorithms that aim to approximate its solution.
- Lecture 2: The theory on the uniqueness of the sparsest solution of a linear system, the notion of stability for the noisy case, guarantees for the performance of pursuit algorithms using the mutual coherence and the RIP.
- Lecture 3: Signal (and image) models and their importance, the Sparseland model and its use, analysis versus synthesis modeling, a Bayesian estimation point of view.
- Lecture 4: First steps in image processing with the Sparseland model - image deblurring, image denoising, image separation, and image inpainting. Global versus local processing of images. Dictionary learnong with the MOD and the K-SVD.
- Lecture 5: Advanced image processing: Using dicitonary learning for image and video denoising and inpainting, image scale-up using a pair of learned dictionaries, Facial image compression with the K-SVD.
The five lectures can be downloaded here (51 MB large).