A Frame Work Using Hyper-Based Methods for Image Registration and Super Resolution

Abstract

High quality-resolution algorithms combine diversified low resolution images into a single Super resolution image. They have received a lot of attention recently in various application domains such as HD Systems, satellite imaging, and video surveillance. These techniques take advantage of the aliasing present in the input images to reconstruct high frequency information of the resulting image. One of the major challenges in such algorithms is a good alignment of the input images: subpixel precision is required to enable accurate reconstruction. In this paper, we give an overview of some subspace techniques that address this problem. We first formulate super-resolution in a multi-channel sampling framework with unknown offsets. Then, we present three registration methods: one approach using ideas from variable projections, one using a Fourier description of the aliased signals, and one using a spline description of the sampling kernel. The performance of the algorithms is evaluated in numerical simulations.

Index Terms— Image registration, Image resolution, Image restoration, Spectral analysis, Spline functions