Universal priors for sparse modeling
Journal Article
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. In this work, we use tools from information theory to propose a sparsity regularization term which has several theoretical and practical advantages over the more standard ℓ or ℓ ones, and which leads to improved coding performance and accuracy in reconstruction tasks. We also briefly report on further improvements obtained by imposing low mutual coherence and Gram matrix norm on the learned dictionaries. © 2009 IEEE. 0 1
Full Text
Duke Authors
Cited Authors
- Raḿrez, I; Lecumberry, F; Sapiro, G
Published Date
- December 1, 2009
Published In
- Camsap 2009 2009 3rd Ieee International Workshop on Computational Advances in Multi Sensor Adaptive Processing
Start / End Page
- 197 - 200
Digital Object Identifier (DOI)
- 10.1109/CAMSAP.2009.5413302
Citation Source
- Scopus