A clustering framework within the sparse modeling and dictionary learning setting is introduced in this work. Instead of searching for the set of centroid that best fit the data, ...
Pablo Sprechmann, Ignacio Ramirez, Guillermo Sapir...
This paper addresses an innovative approach to informed enhancement of damaged sound. It uses sparse approximations with a learned dictionary of atoms modeling the main components...
Manuel Moussallam, Pierre Leveau, Si-Mohamed Aziz ...
We propose an ℓ1 criterion for dictionary learning for sparse signal representation. Instead of directly searching for the dictionary vectors, our dictionary learning approach i...
This article proposes a new method for image separation into a linear combination of morphological components. Sparsity in fixed dictionaries is used to extract the cartoon and osc...
An image representation framework based on structured sparse model selection is introduced in this work. The corresponding modeling dictionary is comprised of a family of learned ...