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Cited by 460

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Learning Multiscale Sparse Representations for Image and Video Restoration

Open access
Mairal, Julien, Sapiro, Guillermo, Elad, Michael·2008·Multiscale Modeling & Simulation

Abstract

This paper presents a framework for learning multiscale sparse representations of color images and video with overcomplete dictionaries. A single-scale K-SVD algorithm was introduced in [M. Aharon, M. Elad, and A. M. Bruckstein, IEEE Trans. Signal Process., 54 (2006), pp. 4311–4322], formulating sparse dictionary learning for grayscale image representation as an optimization problem, efficiently solved via orthogonal matching pursuit (OMP) and singular value decomposition (SVD). Following this work, we propose a multiscale learned representation, obtained by using an efficient quadtree decomposition of the learned dictionary and overlapping image patches. The proposed framework provides an alternative to predefined dictionaries such as wavelets and is shown to lead to state-of-the-art results in a number of image and video enhancement and restoration applications. This paper describes the proposed framework and accompanies it by numerous examples demonstrating its strength.

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Metadata

Authors
Mairal, J., Sapiro, G., & Elad, M.
Venue
Multiscale Modeling & Simulation
Published
2008-01
Volume
7
Issue
1
Pages
214-241
ISSN
  • 1540-3459 (ISSN-L)
  • 1540-3467
OpenAlex
W2056758595
Cited by
460
References
4
FWCI
43.91
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Learning Multiscale Sparse Representations for Image and Video Restoration. https://app.cochat.ai/papers/10.1137/070697653
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Mairal, J., Sapiro, G., & Elad, M. (2008). Learning Multiscale Sparse Representations for Image and Video Restoration. Multiscale Modeling & Simulation. https://doi.org/10.1137/070697653

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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