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Proceedings Paper

Learned dictionaries for sparse image representation: properties and results
Author(s): Karl Skretting; Kjersti Engan
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Paper Abstract

Sparse representation of images using learned dictionaries have been shown to work well for applications like image denoising, impainting, image compression, etc. In this paper dictionary properties are reviewed from a theoretical approach, and experimental results for learned dictionaries are presented. The main dictionary properties are the upper and lower frame (dictionary) bounds, and (mutual) coherence properties based on the angle between dictionary atoms. Both ℓ0 sparsity and ℓ1 sparsity are considered by using a matching pursuit method, order recursive matching Pursuit (ORMP), and a basis pursuit method, i.e. LARS or Lasso. For dictionary learning the following methods are considered: Iterative least squares (ILS-DLA or MOD), recursive least squares (RLS-DLA), K-SVD and online dictionary learning (ODL). Finally, it is shown how these properties relate to an image compression example.

Paper Details

Date Published: 27 September 2011
PDF: 14 pages
Proc. SPIE 8138, Wavelets and Sparsity XIV, 81381N (27 September 2011); doi: 10.1117/12.892684
Show Author Affiliations
Karl Skretting, Univ. of Stavanger (Norway)
Kjersti Engan, Univ. of Stavanger (Norway)


Published in SPIE Proceedings Vol. 8138:
Wavelets and Sparsity XIV
Manos Papadakis; Dimitri Van De Ville; Vivek K. Goyal, Editor(s)

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