Abstract
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.
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URL
https://arxiv.org/abs/2106.15176
