  We present a novel technique to automatically colorize grayscale images that
combine the U-Net model and Fusion Layer features. This approach allows the
model to learn the colorization of images from pre-trained U-Net. Moreover, the
Fusion layer is applied to merge local information results dependent on small
image patches with global priors of an entire image on each class, forming
visually more compelling colorization results. Finally, we validate our
approach with a user study evaluation and compare it against state-of-the-art,
resulting in improvements.
