This course (4 lectures and one tutorial) brings the core ideas and achievements made in the field of sparse and redundant representation modeling, with emphasis on the impact of this field to image processing applications. The five lectures (given as PPTX and PDF) are organized as follows:
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, dictionary learning with the MOD and the K-SVD, global and local image denoising, local image inpainting.
Lecture 4: Sparse representations in image processing – image deblurring, global image separation and image inpainting. using dictionary learning for image and video denoising and inpainting, image scale-up using a pair of learned dictionaries, facial image compression with the K-SVD.