Two Algorithms for Motion Estimation from Alternate Exposure Images

Most algorithms for dense 2D motion estimation assume pairs of images that are acquired with an idealized, infinitively short exposure time. In this work we compare two approaches that use an additional, motion-blurred image of a scene to estimate highly accurate, dense correspondence fields.

We consider video sequences that are acquired with alternating exposure times so that a short-exposure image is followed by a long-exposure image that exhibits motion-blur. For both motion estimation algorithms we employ an image formation model that relates the motion blurred image to two enframing short-exposure images. With this model we can decipher the motion information encoded in the long-exposure image, but also estimate occlusion timings which are a prerequisite for artifact-free frame interpolation. The first approach solves for the motion in a pointwise least squares formulation while the second formulates a global, total variation regularized problem. Both approaches are evaluated in detail and compared to each other and state-of-the-art motion estimation algorithms.