  Automatic image colourisation is the computer vision research path that
studies how to colourise greyscale images (for restoration). Deep learning
techniques improved image colourisation yielding astonishing results. These
differ by various factors, such as structural differences, input types, user
assistance, etc. Most of them, base the architectural structure on
convolutional layers with no emphasis on layers specialised in object features
extraction. We introduce a novel downsampling upsampling architecture named
TUCaN (Tiny UCapsNet) that exploits the collaboration of convolutional layers
and capsule layers to obtain a neat colourisation of entities present in every
single image. This is obtained by enforcing collaboration among such layers by
skip and residual connections. We pose the problem as a per pixel colour
classification task that identifies colours as a bin in a quantized space. To
train the network, in contrast with the standard end to end learning method, we
propose the progressive learning scheme to extract the context of objects by
only manipulating the learning process without changing the model. In this
scheme, the upsampling starts from the reconstruction of low resolution images
and progressively grows to high resolution images throughout the training
phase. Experimental results on three benchmark datasets show that our approach
with ImageNet10k dataset outperforms existing methods on standard quality
metrics and achieves state of the art performances on image colourisation. We
performed a user study to quantify the perceptual realism of the colourisation
results demonstrating: that progressive learning let the TUCaN achieve better
colours than the end to end scheme; and pointing out the limitations of the
existing evaluation metrics.
