Towards Photorealistic Colorization by Imagination
This addresses the problem of producing photorealistic and varied colorizations for users in image processing, though it appears incremental as it builds on existing conditional synthesis networks.
The paper tackles automatic image colorization by imitating human imagination to generate context-correlated color images from black-and-white photos, resulting in more colorful and diverse outputs than state-of-the-art methods.
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.