A method is proposed for super-resolving multi-channel data with applications to PREDATOR video sequences. Using a generalization of Papoulis' sampling theorem, a closed-form solution has been obtained leading to a high-speed algorithm which can be realistically applied to large data sets such as video sequences. In existing multiframe methods it is a common practice to assume that the channel transfer functions are known and invariant from one frame to another, using empirical models such as Gaussian, sinc, etc. We have assumed that the transfer functions are unknown and may vary even when the same sensor is employed, and hence use the observed data to derive the Point Spread Function (PSF) for each frame. The estimated PSFs are used in the super-resolution algorithm. Results on PREDATOR video images are then given.

Abstract

ABSTRACT

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Given a set of low resolution camera images of a Lambertian surface, it is possible to reconstruct high resolution luminance and height information, 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. The problem of surface reconstruction has been tackled in a Bayesian framework and has been formulated as one of minimizing an error function. Markov Random Fields (MRF) have been employed to characterize the a priori conslxaints on the solution space. As for the surface height, we have attempted a direct computation without refering to surface orientations, while increasing the resolution by camera jittering.