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Multiscale Sparse Image Representationwith Learned Dictionaries

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3 Author(s)
Mairal, J. ; Univ. of Minnesota, Minneapolis ; Sapiro, G. ; Elad, M.

This paper introduces a new framework for learning multiscale sparse representations of natural images with overcomplete dictionaries. Our work extends the K-SVD algorithm [1], which learns sparse single-scale dictionaries for natural images. Recent work has shown that the K-SVD can lead to state-of-the-art image restoration results [2, 3]. We show that these are further improved with a multi-scale approach, based on a Quadtree decomposition. Our framework provides an alternative to multiscale pre-defined dictionaries such as wavelets, curvelets, and contourlets, with dictionaries optimized for the data and application instead of pre-modelled ones.

Published in:

Image Processing, 2007. ICIP 2007. IEEE International Conference on  (Volume:3 )

Date of Conference:

Sept. 16 2007-Oct. 19 2007

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