(4.2 MB) Abstract
Image colourisation is the task of adding plausible colour to grayscale images. This transformation requires obtaining a three dimensional colour-valued mapping from a real-valued grayscale image, which leads to an undetermined problem because the gray-scale semantics and texture provide cues for multiple possible colour mappings. The goal of image colourisation in not to recover the ground truth colour in a manner that it is perceived as natural by a human observer. Our work takes as a baseline a scheme based on an end-to-end trainable convolutional neural network (CNN) trained with a smooth L1 loss to predict the $ab$ channels of a colour image given the $L$ channel. We introduce an extra perceptual reconstruction loss during training to improve the capabilities of a adversarial adversarial model, that we adopt as a baseline.
