SUB-PIXEL BAYESIAN-ESTIMATION OF ALBEDO AND HEIGHT

Given a set of low resolution camera images of a Lambertian surface, i t is possible to reconstruct high resolution luminance and height info rmation, when the relative displacements of the image frames are known . We have proposed iterative algorithms for recovering high resolution albedo with the knowledge of high resolution height and vice versa. T he problem of surface reconstruction has been tackled in a Bayesian fr amework and has been formulated as one of minimizing an error function . Markov Random Fields (MRF) have been employed to characterize the a priori constraints on the solution space. As for the surface height, w e have attempted a direct computation without refering to surface orie ntations, while increasing the resolution by camera jittering.