March 21, Wednesday 14:15, Room 303, Jacobs Building

Title: Sparse and Redundant Representation Modeling of Images: Theory and Applications

Lecturer: Michael Elad

Lecturer homepage : http://www.cs.technion.ac.il/~elad/

Affiliation : Technion

This talk focuses on the use of sparse and redundant representations and learned dictionaries for image denoising and other related problems in image processing. We discuss the the K-SVD algorithm for learning a dictionary that describes the image content efficiently. We then show how to harness this algorithm for image denoising, by working on small patches and forcing sparsity over the trained dictionary. The above is extended to color image denoising and inpainting, video denoising, and facial image compression, leading in all these cases to state of the art results. We conclude with more recent results on the use of several sparse representations for getting better denoising performance. An algorithm to generate such set of representations is developed, and our analysis shows that by this we approximate the minimum-mean-squared-error (MMSE) estimator, thus getting better results.