Improved Image Registration using the Up-Sampled Domain
We consider the warping problem which appears in well-known image registration algorithms that use higher-order motion models. An implementation inspired by recent work and a new image registration algorithm are used for the analysis. Both approaches rely on frame-to-frame estimation. The key technique is the well-established gradient descent approach for the estimation of higher-order motion parameters. We show that using up-sampled input images in the last step of the algorithms improves the accuracy of the estimated motion parameters. It can be seen in the experimental results that the performance of the image registration algorithms increases significantly only by applying the gradient descent on up-sampled images in comparison to recent algorithms developed