Colorization has attracted increasing interest in recent years. Classic reference-based methods usually rely on external color images for plausible results. A large image database or online search engine is inevitably required for retrieving such exemplars. Recent deep-learning-based methods could automatically colorize images at a low cost. However, unsatisfactory artifacts and incoherent colors are always accompanied. In this work, we propose GCP-Colorization that leverages the rich and diverse color priors encapsulated in a pretrained Generative Adversarial Networks (GAN) for automatic colorization. Specifically, we first "retrieve" matched features (similar to exemplars) via a GAN encoder and then incorporate these features into the colorization process with feature modulations. Thanks to the powerful generative color prior (GCP) and delicate designs, our GCP-Colorization could produce vivid colors with a single forward pass. Moreover, it is highly convenient to obtain diverse results by modifying GAN latent codes. GCP-Colorization also inherits the merit of interpretable controls of GANs and could attain controllable and smooth transitions by walking through GAN latent space. Extensive experiments and user studies demonstrate that GCP-Colorization achieves superior performance than previous works. Codes are available at https://github.com/ToTheBeginning/GCP-Colorization.
翻译:近年来,以传统参考为基础的方法通常依靠外部色彩图像来获取可信的结果。为了检索这些示例,必然需要一个大型图像数据库或在线搜索引擎。最近的深学习方法可以以低成本自动颜色化图像。然而,不尽人意的工艺品和不相容的颜色总是随附的。在这项工作中,我们建议GCP-Cororization利用预先培训过的基因对冲网络(GAN)中所含的丰富和多样化的颜色前科来自动颜色化。具体地说,我们首先需要通过 GAN 编码编码来“ 检索” 匹配功能( 类似于Exemplakers ), 然后将这些特征以特性调制成成色彩化进程。由于先前的强势配色( GCP) 和微妙的设计, 我们的GCP- Cloorization 能够用一个前端通道来产生生动的颜色。此外,通过修改GAN 隐含代码( GCP- Colorization) 也可以继承GAN 的可解释性控性控制功能,然后在GAN 上进行可操作的升级和平稳的操作。