  The remastering of vintage film comprises of a diversity of sub-tasks
including super-resolution, noise removal, and contrast enhancement which aim
to restore the deteriorated film medium to its original state. Additionally,
due to the technical limitations of the time, most vintage film is either
recorded in black and white, or has low quality colors, for which colorization
becomes necessary. In this work, we propose a single framework to tackle the
entire remastering task semi-interactively. Our work is based on temporal
convolutional neural networks with attention mechanisms trained on videos with
data-driven deterioration simulation. Our proposed source-reference attention
allows the model to handle an arbitrary number of reference color images to
colorize long videos without the need for segmentation while maintaining
temporal consistency. Quantitative analysis shows that our framework
outperforms existing approaches, and that, in contrast to existing approaches,
the performance of our framework increases with longer videos and more
reference color images.
