A Fast Image Registration Approach based on SIFT Key-points Applied to Super-Resolution

An accurate image registration is a fundamental stage in many image processing problems. In this paper a new and fast registration approach based on Scale Invariant Feature Transform keypoints descriptors, under Euclidean transformation model is proposed. The core idea of the proposed method is estimation of rotation angle and vertical and horizontal shifts using averaging of differences of SIFT key-points pairs descriptors. The method is simple but requires some tuning modules for accurate estimation. Orientation modification and compensation and shift compensation are some of the proposed modules. The proposed method is fast, it is about 5 times faster than RANSAC method for model parameters estimation. The accuracy of the proposed method is compared with some popular registration methods. Various comparisons have been done with LIVE database images with known motion vectors. The experimental results show the high performance of the proposed algorithm in a super-resolution application.