Image Registration via Geometrically Constrained Total Variation Optical Flow

In this paper, we present a method for registration of image pairs. Our method relates both images to one another for registration purposes making use of optical flow. We formulate the problem in a variational setting making use of an L1-norm fidelity term, a total variation (TV) criterion, and a geometric constraint. This treatment leads to a cost function, in which, both the total variation and the homographic constraints are enforced via regularisation. Further, to compute the flow we employ a multiscale pyramid, whereby the total variation is minimized at each layer and the geometric constraint is enforced between layers. In practice, this is carried out by using a Rudin-Osher-Fatemi (ROF) denoising model within each layer and a gated function for the homography computation between layers. We also illustrate the utility of our method for image registration and flow computation and compare our approach to a mainstream non-geometrically constrained variational alternative elsewhere in the literature.