In plain words: The system first imagines several colorful versions of a scene based on what it sees in a black-and-white photo, then uses those imagined colors to guide coloring the original picture. It produced more colorful and varied results than the best existing colorization tools.
Abstract
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.
Chenyang Lei, Yue Wu, Qifeng Chen
arXiv:2108.09195 · cs.CV · submitted Aug 20, 2021
abstract · pdf · html · NeurIPS 2021 submission