  There are large amount of valuable video archives in Video Home System (VHS)
format. However, due to the analog nature, their quality is often poor.
Compared to High-definition television (HDTV), VHS video not only has a dull
color appearance but also has a lower resolution and often appears blurry. In
this paper, we focus on the problem of translating VHS video to HDTV video and
have developed a solution based on a novel unsupervised multi-task adversarial
learning model. Inspired by the success of generative adversarial network (GAN)
and CycleGAN, we employ cycle consistency loss, adversarial loss and perceptual
loss together to learn a translation model. An important innovation of our work
is the incorporation of super-resolution model and color transfer model that
can solve unsupervised multi-task problem. To our knowledge, this is the first
work that dedicated to the study of the relation between VHS and HDTV and the
first computational solution to translate VHS to HDTV. We present experimental
results to demonstrate the effectiveness of our solution qualitatively and
quantitatively.
