  Human beings can imagine the colours of a grayscale image with no particular
effort thanks to their ability of semantic feature extraction. Can an
autonomous system achieve that? Can it hallucinate plausible and vibrant
colours? This is the colourisation problem. Different from existing works
relying on convolutional neural network models pre-trained with supervision, we
cast such colourisation problem as a self-supervised learning task. We tackle
the problem with the introduction of a novel architecture based on Capsules
trained following the adversarial learning paradigm. Capsule networks are able
to extract a semantic representation of the entities in the image but loose
details about their spatial information, which is important for colourising a
grayscale image. Thus our UCapsNet structure comes with an encoding phase that
extracts entities through capsules and spatial details through convolutional
neural networks. A decoding phase merges the entity features with the spatial
features to hallucinate a plausible colour version of the input datum. Results
on the ImageNet benchmark show that our approach is able to generate more
vibrant and plausible colours than exiting solutions and achieves superior
performance than models pre-trained with supervision.
