Sparse and Redundant Representation Modeling for Image Processing
December 11th, 2008
Computaitonal Algebraic Statistics, Theory and Applications (CASTA), Kyoto, Japan.

In this talk we describe applications such as image denoising and beyond using sparse and redundant representations. Our focus is on ways to perform these tasks with trained dictionaries using the K-SVD algorithm. As trained dictionaries are limited in handling small image patches, we deploy these within a Bayesian reconstruction procedure by forming an image prior that forces every patch in the resulting image to have a sparse representation.

Invited talk. Joint work with Michal Aharon (CS - Technion, Guillermo Sapiro (UMN), Julien Mairal (Inria - France), and Matan Protter (CS - Technion).