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.
翻译:通过模仿人类专家的想象过程,我们展示了一种新颖的自动图像色彩化方法。 我们的想象模块旨在生成与黑白照片相关背景的彩色图像。 在黑白图像中, 我们的想象模块首先提取了上下文信息, 然后用一个有条件的图像合成网络( 如语义图像合成模型) 来合成多彩和多样的图像。 然后我们设计了一个彩色模块, 将黑白图像颜色化, 并用光现实化的想象力指导。 实验结果显示, 我们的工作产生了比最新图像颜色化方法更丰富多彩和多样的结果。 我们的源代码将公开提供。