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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Rapport
RR-2706, INRIA. 1995
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Dernière modification le : samedi 27 janvier 2018 - 01:31:30
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Hassan Shekarforoush, Marc Berthod, Josiane Zerubia. 3D Super-resolution Using Generalised Sampling Expansion. RR-2706, INRIA. 1995. 〈inria-00073984〉

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