Disparity estimation from multicomponent images under illumination variation

  • Description: Disparity map can be recovered by estimating the relative position of features in the stereo pair. This process is called stereo matching. Looking for correspondences between stereo images is a difficult task, because of the presence of hidden areas (i.e. occlusions) and because of the fact that the light is reflected differently depending on the viewing angle. In this toolbox, we adress the problem of stereo matching of multi-component images (e.g. color images) by jointly estimating the disparity and the illumination variation. A convex energy function that takes into account the illumination variation model is derived by resorting to a relaxation based on a first-order Taylor approximation around an initial estimate. This energy is then minimized while taking into consideration various convex constraints arising from prior knowledge and observed data. The proposed method allows us to incorporate various convex distances and it relies on the extension of the Parallel ProXimal Algorithm (PPXA).

  • Download: Disparity_under_ill.zip

  • Associated journal paper: C. Chaux, M. El Gheche, J. Farah, J.-C. Pesquet, and B. Pesquet- Popescu, A parallel proximal splitting method for disparity estimation from multicomponent images under illumination variation, Journal of Mathematical Imaging and Vision, vol. 47, no. 3, pages 167-178, 2013.


Disparity estimation

  • Description: In this toolbox, we consider the problem of disparity estimation from gray level stereo images without illumination variation.

  • Download: Disparity.zip

  • Associated paper: M. El Gheche, J.-C. Pesquet, J. Farah, M. Kaaniche and B. Pesquet-Popescu, “Proximal splitting methods for depth estimation,” in ICASSP 2011, Prague, Czech Republic, 22-27 May 2011.