A fast super-resolution reconstruction algorithm for pure translational motion and common space-invariant blur

Super-resolution reconstruction deals with the recovery of a single high-resolution image from a set of warped, blurred and decimated versions of it. Several algorithms were already proposed for the solution of this general problem. We concentrate on a special case where the warps are pure translations, the blur is space invariant and the same for all the images, and the noise is white. We exploit previous results to develop a new highly efficient super-resolution reconstruction algorithm for this case, which separates the treatment to de-blurring and measurements fusion. The fusion part is shown to be a non-iterative and very simple algorithm, preserving the optimality of the entire reconstruction process, in the maximum-likelihood sense. Simulations demonstrate the capabilities of the proposed algorithm.