  Image interpolation, or image morphing, refers to a visual transition between
two (or more) input images. For such a transition to look visually appealing,
its desirable properties are (i) to be smooth; (ii) to apply the minimal
required change in the image; and (iii) to seem "real", avoiding unnatural
artifacts in each image in the transition. To obtain a smooth and
straightforward transition, one may adopt the well-known Wasserstein Barycenter
Problem (WBP). While this approach guarantees minimal changes under the
Wasserstein metric, the resulting images might seem unnatural. In this work, we
propose a novel approach for image morphing that possesses all three desired
properties. To this end, we define a constrained variant of the WBP that
enforces the intermediate images to satisfy an image prior. We describe an
algorithm that solves this problem and demonstrate it using the sparse prior
and generative adversarial networks.
