Open Access

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

  • Sylvain Fischer1, 2Email author,
  • Rafael Redondo1,
  • Laurent Perrinet2 and
  • Gabriel Cristóbal1
EURASIP Journal on Advances in Signal Processing20062007:090727

Received: 1 December 2005

Accepted: 18 September 2006

Published: 28 December 2006


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.


Approximation AlgorithmVisual CortexReceptive FieldApproximation SchemeImage Compression


Authors’ Affiliations

Instituto de Óptica- CSIC, Madrid, Spain
INCM, UMR 6193, CNRS and Aix-Marseille University, Marseille Cedex 20, France


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© Sylvain Fischer et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.