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3D Super-resolution Using Generalised Sampling Expansion

Abstract : Using a probabilistic interpretation of Papoulis' generalized sampl ing theorem, an iterative algorithm has been devised for 3D reconstruction of a Lambertian surface at sub-pixel accuracy. The problem has been formulated as a n optimization one in a Bayesian framework. The latter allows for introducing { \em a priori} information on the solution, using Markov Random Fields (MRF). Th e estimated 3D features of the surface are the albedo and the height which are obtained simultaneously using a set of low resolution images.
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Contributor : Rapport de Recherche Inria <>
Submitted on : Wednesday, May 24, 2006 - 2:14:26 PM
Last modification on : Saturday, January 27, 2018 - 1:31:30 AM
Long-term archiving on: : Thursday, March 24, 2011 - 1:38:36 PM


  • HAL Id : inria-00073984, version 1



Hassan Shekarforoush, Marc Berthod, Josiane Zerubia. 3D Super-resolution Using Generalised Sampling Expansion. RR-2706, INRIA. 1995. ⟨inria-00073984⟩