We present a novel approach to automatic image colorization by imitating the imagination process of human experts. Our imagination module is designed to generate color images that are context-correlated with black-and-white photos. Given a black-and-white image, our imagination module firstly extracts the context information, which is then used to synthesize colorful and diverse images using a conditional image synthesis network (e.g., semantic image synthesis model). We then design a colorization module to colorize the black-and-white images with the guidance of imagination for photorealistic colorization. Experimental results show that our work produces more colorful and diverse results than state-of-the-art image colorization methods. Our source codes will be publicly available.
@article{arxiv.2108.09195,
title = {Towards Photorealistic Colorization by Imagination},
author = {Chenyang Lei and Yue Wu and Qifeng Chen},
journal= {arXiv preprint arXiv:2108.09195},
year = {2021}
}