An efficient approach for registration and super-resolution of aliased images

This paper addresses the problem of reconstructing a super- resolved image from a set of aliased, noisy, low-resolution (LR), and blurry images. The accurate knowledge of the sub-pixel registration parameters for each LR image is the key for this problem. However, the presence of aliasing is the main challenge in registering these images. In this paper, we analyze the method which combines the registration problem into the super-resolution reconstruction (SSR) and propose a novel approach to solve it. The proposed approach utilizes the principle similar to the variable projection method, which results in a better-conditioned problem and avoids some shortcomings of cyclic coordinate descent optimization procedure. It can be efficiently implemented by using Lanezos algorithm and Gauss quadrature theory. As a result, the proposed approach can deal with translation and rotation between the observed LR images. Experimental results demonstrate the effectiveness of our approach.