3D super-resolution using generalized sampling expansion

  title={3D super-resolution using generalized sampling expansion},
  author={Hassan Shekarforoush and Marc Berthod and Josiane Zerubia},
A 3D super-resolution algorithm is proposed below, based on a probabilistic interpretation of the ndimensional version of Papoulis’ 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. 


Publications referenced by this paper.
Showing 1-10 of 19 references

Image analysis and markov elds

R. Azencott
Int. Conf. Ind. & Appl. Math, Paris • 1987
View 7 Excerpts
Highly Influenced

Digital image restoration: A survey

Computer • 1974
View 6 Excerpts
Highly Influenced


D. S. Simonett, F. T. Ulaby
Manual of Remote Sensing, volume 1. American Society of Photogrammetry, second edition • 1983
View 3 Excerpts
Highly Influenced

Subpixel accuracy image registration by spectrum cancellation

S. P. Kim, Wen-Yu Su
In Proc. ICASSP, • 1993
View 2 Excerpts

and J

M. Berthod, H. Shekarforoush, M. Werman
Zerubia. Reconstruction of high resolution 3d visual information • 1993
View 3 Excerpts

Papoulis' generalization of the sampling theorem in higher dimensions and its applications to sample density reduction

K. F. Cheung, R. J. Marks
Proc. Int. Conf. on circuits and systems, Nanjing, China • 1989
View 1 Excerpt

Similar Papers

Loading similar papers…