Image Alignment using Norm Conserved GAT Correlation

This paper describes a new area-based image alignment technique, norm conserved GAT (Global Affine Transformation) correlation. The cutting-edge techniques of image alignment are mostly feature-based, such well-known techniques as SIFT, SURF, ASIFT, and ORB. The proposed technique determines affine parameters maximizing ZNCC (zero-means normalized cross-correlation) between warped and reference images. In experiments using artificially warped images subject to rotation, blur, random noise, a few kinds of general affine transformation, and a simple 2D projection transformation, we compare the proposed technique against the feature-based ORB (Oriented FAST and Rotated BRIEF), the competing area based ECC (Enhanced Correlation Coefficient), the original GAT correlation, and the GPT (Global Projection Transformation) correlation techniques. We show a very promising ability of the proposed norm conserved GAT correlation by discussing the advantages and disadvantages of these techniques with respect to both ability of image alignment and computational complexity.