3D super-resolution using generalized sampling expansion

Author

Shekarforoush, H. ; Berthod, M. ; Zerubia, J.

Author_Institution

Inst. Nat. de Recherche en Inf. et Autom., Sophia Antipolis, France

Volume

2

fYear

1995

fDate

23-26 Oct 1995

Firstpage

300

Abstract

Using a set of low resolution images it is possible to reconstruct high resolution information by merging low resolution data on a finer grid. A 3D super-resolution algorithm is proposed, based on a probabilistic interpretation of the n-dimensional version of Papoulis´ (1977) generalized sampling theorem. The algorithm is devised for recovering the albedo and the height map of a Lambertian surface in a Bayesian framework, using Markov random fields for modeling the a priori knowledge

Keywords

Bayes methods; Markov processes; image reconstruction; image resolution; image sampling; random processes; 3D superresolution algorithm; Bayesian framework; Lambertian surface; Markov random fields; generalized sampling expansion; generalized sampling theorem; height map; high resolution information; image reconstruction; image resolution; low resolution images; probabilistic interpretation; Bayesian methods; Cost function; Image reconstruction; Image resolution; Image sampling; Iterative algorithms; Markov random fields; Merging; Sampling methods; Surface reconstruction;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 1995. Proceedings., International Conference on

Conference_Location

Washington, DC

Print_ISBN

0-8186-7310-9

Type

conf

DOI

10.1109/ICIP.1995.537474

Filename

537474