Universal regularizers for robust sparse coding and modeling.

Sparse data models, where data is assumed to be well represented as a linear combination of a few elements from a dictionary, have gained considerable attention in recent years, and their use has led to state-of-the-art results in many signal and image processing tasks. It is now well understood that the choice of the sparsity regularization term is critical in the success of such models. Based on a codelength minimization interpretation of sparse coding, and using tools from universal coding theory, we propose a framework for designing sparsity regularization terms which have theoretical and practical advantages when compared with the more standard l(0) or l(1) ones. The presentation of the framework and theoretical foundations is complemented with examples that show its practical advantages in image denoising, zooming and classification.

Full Text

Duke Authors

Cited Authors

  • Ramírez, I; Sapiro, G

Published Date

  • September 2012

Published In

Volume / Issue

  • 21 / 9

Start / End Page

  • 3850 - 3864

PubMed ID

  • 22562754

Electronic International Standard Serial Number (EISSN)

  • 1941-0042

Digital Object Identifier (DOI)

  • 10.1109/TIP.2012.2197006

Language

  • eng