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Universal regularizers for robust sparse coding and modeling.

Publication ,  Journal Article
Ramírez, I; Sapiro, G
Published in: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
September 2012

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

Published In

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

DOI

EISSN

1941-0042

ISSN

1057-7149

Publication Date

September 2012

Volume

21

Issue

9

Start / End Page

3850 / 3864

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

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Ramírez, I., & Sapiro, G. (2012). Universal regularizers for robust sparse coding and modeling. IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society, 21(9), 3850–3864. https://doi.org/10.1109/tip.2012.2197006
Ramírez, Ignacio, and Guillermo Sapiro. “Universal regularizers for robust sparse coding and modeling.IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society 21, no. 9 (September 2012): 3850–64. https://doi.org/10.1109/tip.2012.2197006.
Ramírez I, Sapiro G. Universal regularizers for robust sparse coding and modeling. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2012 Sep;21(9):3850–64.
Ramírez, Ignacio, and Guillermo Sapiro. “Universal regularizers for robust sparse coding and modeling.IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society, vol. 21, no. 9, Sept. 2012, pp. 3850–64. Epmc, doi:10.1109/tip.2012.2197006.
Ramírez I, Sapiro G. Universal regularizers for robust sparse coding and modeling. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2012 Sep;21(9):3850–3864.

Published In

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

DOI

EISSN

1941-0042

ISSN

1057-7149

Publication Date

September 2012

Volume

21

Issue

9

Start / End Page

3850 / 3864

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