Blind cross-spectral image registration using prefiltering and Fourier-based translation detection

This paper describes a fast technique for registering image pairs from visible and infrared spectra that differ by translation, small rotations, and small changes of scale. The main result of this paper is a nonlinear prefiltering and thresholding technique that substantially enhances the cross-spectral correlation, provided that the image pairs have many features in common. We show the use of the technique in conjunction with a Fourier-based normalized correlation algorithm to perform fast cross-spectral registrations. In the absence of such prefiltering, local reversals of contrast from image to image tend, to impair the quality of correlation-based registrations. The algorithm computes the translation that maximizes the overall normalized correlation of the filtered images without examining individual image features. Small rotations and scale changes can be recovered by computing the translation displacement in several different regions of the image pairs. In an experiment on a moderate-sized database, the algorithm produced a correct registration rate of over 90% at a false positive rate of less than 10%. For a particularly difficult subset of images in the database, the correct registration rate fell to approximately 85% at a false positive rate of less than 10%. This retrieval quality is comparable to that of a mutual-information-based algorithm