TexGeoPPXA_Lab : Matlab toolbox for geometry/texture decomposition

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
Higher Edu - Research dev card
  • Creation or important update: 04/03/11
  • Minor correction: 25/03/11
  • Index card author: Caroline Chaux (LATP)
  • Theme leader: Teresa Gomez-Diaz (LIGM)
Keywords
General software features

This software performs geometry/texture decomposition from a degraded observation. By degradation we mean convolution operator and Poisson noise.
The method is based on convex criterion minimization. The criterion contains 4 terms :

  • Kullback-Leibler divergence
  • indicator function
  • l1 norm applied on frame coefficients (texture component)
  • total variation applied on geometry component

The PPXA (Parallel ProXimal Algorithm) is used to perform the minimization.

Context in which the software is used

This software performs geometry/texture decomposition from a degraded (convolution + Poisson noise) observation.

Publications related to software
  • N. Pustelnik, Méthodes proximales pour la résolution de problèmes inverses. Application à la Tomographie par Emission de Positrons. Thèse Université Paris-Est, 2010.
  • L. M. Briceno-Arias, P. L. Combettes, J.-C. Pesquet, and N. Pustelnik, Proximal method for geometry and texture image decomposition, International Conference on Image Processing (ICIP) , Honk Kong, 26-29 Septembre 2010.
  • http://nellypustelnik.perso.sfr.fr/