Implicit similarity: a new approach to multi-sensor image registration

This paper presents an implicit similarity-based approach to registration of significantly dissimilar images, acquired by sensors at different modalities. The proposed algorithm introduces a robust matching criterion by aligning the locations of gradient maxima. The alignment is formulated as a parametric variational optimization problem, which is solved iteratively by considering the intensities of a single image. The location of the maxima of the second image´s gradient are used as initialization. We are able to robustly estimate affine and projective global motions using ´coarse to fine´ processing, even when the images are characterized by complex space varying intensity transformations. Finally, we present the registration of real images, which were taken by multi-sensor and multi-modality using affine and projective motion models.