Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Sparse Approximation of Images Inspired from the Functional Architecture of the Primary Visual Areas | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 90727 16 pages doi 2007 90727 Research Article Sparse Approximation of Images Inspired from the Functional Architecture of the Primary Visual Areas Sylvain Fischer 1 2 Rafael Redondo 1 Laurent Perrinet 2 and Gabriel Cristobal1 1 Institute de Optica - CSIC Serrano 121 28006 Madrid Spain 2 INCM UMR 6193 CNRS and Aix-Marseille University 31 chemin Joseph Aiguier 13402 Marseille Cedex20 France Received 1 December 2005 Revised 7 September 2006 Accepted 18 September 2006 Recommended by Javier Portilla 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. Copyright 2007 Sylvain Fischer et al. 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. 1. INTRODUCTION Recent works on multiresolution transforms showed the necessity of using overcomplete transformations to solve drawbacks of bi- orthogonal wavelets namely their lack
