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Sparse Approximation of Images Inspired from the Functional Architecture of the Primary Visual Areas

EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 090727 (2006) Cite this article

Abstract

Several drawbacks of critically sampled wavelets can be solved by overcomplete multiresolution transforms and sparse approximation algorithms. Facing the difficulty to optimize such nonorthogonal and nonlinear transforms, we implement a sparse approximation scheme inspired from the functional architecture of the primary visual cortex. The scheme models simple and complex cell receptive fields through log-Gabor wavelets. The model also incorporates inhibition and facilitation interactions between neighboring cells. Functionally these interactions allow to extract edges and ridges, providing an edge-based approximation of the visual information. The edge coefficients are shown sufficient for closely reconstructing the images, while contour representations by means of chains of edges reduce the information redundancy for approaching image compression. Additionally, the ability to segregate the edges from the noise is employed for image restoration.

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Authors and Affiliations

  1. Instituto de Óptica- CSIC, Serrano 121, Madrid, 28006, Spain

    Sylvain Fischer, Rafael Redondo & Gabriel Cristóbal

  2. INCM, UMR 6193, CNRS and Aix-Marseille University, 31 chemin Joseph Aiguier, Marseille Cedex 20, 13402, France

    Sylvain Fischer & Laurent Perrinet

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  1. Sylvain Fischer
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  2. Rafael Redondo
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  3. Laurent Perrinet
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  4. Gabriel Cristóbal
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Correspondence to Sylvain Fischer.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Fischer, S., Redondo, R., Perrinet, L. et al. Sparse Approximation of Images Inspired from the Functional Architecture of the Primary Visual Areas. EURASIP J. Adv. Signal Process. 2007, 090727 (2006). https://doi.org/10.1155/2007/90727

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