Metric-Based Pairwise and Multiple Image Registration

Qian Xie, Sebastian Kurtek, Eric Klassen, Gary E. Christensen, Anuj Srivastava

Abstract: Registering pairs or groups of images is a widely-studied problem that has seen a variety of solutions in recent years. Most of these solutions are variational, using objective functions that should satisfy several basic and desired properties. In this paper, we pursue two additional properties – (1) invariance of objective function under identical warping of input images and (2) the objective function induces a proper metric on the set of equivalence classes of images – and motivate their importance. Then, a registration framework that satisfies these properties, using the L 2-norm between a novel representation of images, is introduced. Additionally, for multiple images, the induced metric enables us to compute a mean image, or a template, and perform joint registration. We demonstrate this framework using examples from a variety of image types and compare performances with some recent methods.