A Fast Super-Resolution Reconstruction Algorithm for Pure Translation Motion and Common Space-Invariant Blur

super-resolution, reconstruction, maximum-likelihood, translation motion The super-resolution reconstruction problem is an inverse problem, dealing with the recovery of a single high-resolution image from a set of low quality images. In its general form, the superresolution problem may consist of images with arbitrary geometric warp, space variant blur and colored noise. Several algorithms were already proposed for the solution of this general problem. In this paper we concentrate on a special case of the superresolution problem, where the warp is composed of pure translation, the blur is space invariant and constant for all the measured images, and the additive noise is a white Gaussian noise. We exploit our previous results, and develop a new highly efficient super-resolution reconstruction algorithm for this case. This algorithm separates the treatment of the blur from the fusion of the measurements, and the resulting overall algorithm is non-iterative. The proposed algorithm is compared to known algorithms in the literature, showing that it is superior in terms of computational complexity. Simulations demonstrate the capabilities of the proposed algorithm.