This paper addresses the automatic colorization problem, which converts a gray-scale image to a colorized one. Recent deep-learning approaches can colorize automatically grayscale images. However, when it comes to different scenes which contain distinct color styles, it is difficult to accurately capture the color characteristics. In this work, we propose a fully automatic colorization approach based on Symmetric Positive Definite (SPD) Manifold Learning with a generative adversarial network (SPDGAN) that improves the quality of the colorization results. Our SPDGAN model establishes an adversarial game between two discriminators and a generator. The latter is based on ResNet architecture with few alterations. Its goal is to generate fake colorized images without losing color information across layers through residual connections. Then, we employ two discriminators from different domains. The first one is devoted to the image pixel domain, while the second one is to the Riemann manifold domain which helps to avoid color misalignment. Extensive experiments are conducted on the Places365 and COCO-stuff databases to test the effect of each component of our SPDGAN. In addition, quantitative and qualitative comparisons with state-of-the-art methods demonstrate the effectiveness of our model by achieving more realistic colorized images with less artifacts visually, and good results of PSNR, SSIM, and FID values.

翻译：本文针对自动着色问题展开研究，该问题旨在将灰度图像转化为彩色图像。当前深度学习方法可自动完成灰度图像着色，但当面对包含不同色彩风格的多场景图像时，难以准确捕捉色彩特征。为此，我们提出一种基于对称正定流形学习与生成对抗网络的全自动着色方法（SPDGAN），有效提升着色质量。该模型构建了两个判别器与一个生成器之间的对抗博弈：生成器基于改良的ResNet架构，通过残差连接在跨层传输中保持色彩信息，生成逼真着色图像；两个判别器分别从图像像素域和黎曼流形域进行鉴别，后者可有效避免色彩错位。在Places365和COCO-stuff数据库上的大量实验验证了SPDGAN各组件的效能。与现有先进方法的定量及定性比较表明，本模型生成的着色图像更逼真、伪影更少，且PSNR、SSIM与FID指标表现优异。