Learned dictionaries for sparse image representation: properties and results

Karl Skretting; Kjersti Engan

doi:10.1117/12.892684

27 September 2011 Learned dictionaries for sparse image representation: properties and results

Karl Skretting, Kjersti Engan

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Proceedings Volume 8138, Wavelets and Sparsity XIV; 81381N (2011) https://doi.org/10.1117/12.892684
Event: SPIE Optical Engineering + Applications, 2011, San Diego, California, United States

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.

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Karl Skretting and Kjersti Engan "Learned dictionaries for sparse image representation: properties and results", Proc. SPIE 8138, Wavelets and Sparsity XIV, 81381N (27 September 2011); https://doi.org/10.1117/12.892684

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