Abstract
Given a set of low resolution camera images, it is possible to
reconstruct high resolution luminance and depth information, specially
if the relative displacements of the image frames are known. We propose
iterative algorithms for recovering hash resolution albedo and depth
maps that require no a priori knowledge of the scene, and therefore do
not depend on other methods, as regards boundary and initial conditions.
The problem of surface reconstruction has been formulated as one of
expectation maximization (EM) and has been tackled in a probabilistic
framework using Markov random fields (MRF). As for the depth map, our
method directly recovers surface heights without refering to surface
orientations, while increasing the resolution by camera jittering.
Conventional statistical models have been coupled with geometrical
techniques to construct a general model of the world and the imaging
process
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