In the literature there has been much research into two methods of attacking
the super-resolution problem: using optical flow-based techniques to align
low-resolution images as samples of a target high-resolution image, and using
learning-based techniques to estimate perceptually-plausible high frequency
components of a low-resolution image. Both of these approaches have been
naturally extended to apply to image sequences from video, yet heretofore
there have been no investigations into combining these methods to obviate
problems associated with each method individually. We show how to merge
these two disparate approaches to attack two problems associated with
super-resolution for video: removing temporal artifacts ("flicker") and
improving image quality.