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.
Duke Scholars
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Related Subject Headings
- Artificial Intelligence & Image Processing
- 4607 Graphics, augmented reality and games
- 4603 Computer vision and multimedia computation
- 1702 Cognitive Sciences
- 0906 Electrical and Electronic Engineering
- 0801 Artificial Intelligence and Image Processing
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Artificial Intelligence & Image Processing
- 4607 Graphics, augmented reality and games
- 4603 Computer vision and multimedia computation
- 1702 Cognitive Sciences
- 0906 Electrical and Electronic Engineering
- 0801 Artificial Intelligence and Image Processing