A parallel proximal splitting method for disparity estimation from multicomponent images under illumination variation

Caroline Chaux 1 Mireille El Gheche 1 Joumana Farah 2 Jean-Christophe Pesquet 1 Beatrice Pesquet-Popescu 3
1 LIGM - Laboratoire d'Informatique Gaspard-Monge
2 Department of Telecommunications, Faculty of Engineering
3 LTCI - Laboratoire Traitement et Communication de l'Information
Abstract : Proximal splitting algorithms play a central role in finding the numerical solution of convex optimization problems. This paper addresses the problem of stereo matching of multi-component images by jointly estimating the disparity and the illumination variation. The global formulation being non-convex, the problem is addressed by solving a sequence of convex relaxations. Each convex relaxation is non trivial and involves many constraints aiming at imposing some reg- ularity on the solution. Experiments demonstrate that the method is efficient and provides better results compared with other approaches.
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ENPC | LIGM_SIGNAL | LIGM | PARISTECH | INSTITUT-TELECOM | UNIV-MLV | UPEC-UPEM | LTCI

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Caroline Chaux, Mireille El Gheche, Joumana Farah, Jean-Christophe Pesquet, Beatrice Pesquet-Popescu. A parallel proximal splitting method for disparity estimation from multicomponent images under illumination variation. Journal of Mathematical Imaging and Vision, Springer Verlag, 2013, 47 (3), pp.167-178. ⟨10.1007/s10851-012-0361-z⟩. ⟨hal-00733454⟩

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