Edge-Aware Multi-Scale Progressive Colorization

Image colorization recovers a colorful image from a grayscale one. Trained by large-scale datasets, recent deep neural networks based methods can produce impressive colorful images. However, they usually directly train a single network using training images of fixed resolution. It is hard for such a single network to learn the features of different scales for colorization. Moreover, they are prone to generate color bleedings and blurry details around objects boundaries. To address these problems, we propose a novel edge-aware multi-scale progressive network (EMSPN). The key idea is to train a series of multi-scale networks in a progressive manner, so that the network in finer scales can leverage the outputs of its previous scale. In addition, we also propose an edge-map loss to effectively prevent bleedings and blurs around the image edges. Experimental results show that our work outperforms existing methods and achieves state-of-the-art results.