The Japanese comic format known as Manga is popular all over the world. It is traditionally produced in black and white, and colorization is time consuming and costly. Automatic colorization methods generally rely on greyscale values, which are not present in manga. Furthermore, due to copyright protection, colorized manga available for training is scarce. We propose a manga colorization method based on conditional Generative Adversarial Networks (cGAN). Unlike previous cGAN approaches that use many hundreds or thousands of training images, our method requires only a single colorized reference image for training, avoiding the need of a large dataset. Colorizing manga using cGANs can produce blurry results with artifacts, and the resolution is limited. We therefore also propose a method of segmentation and color-correction to mitigate these issues. The final results are sharp, clear, and in high resolution, and stay true to the character's original color scheme.

本研究提出一种基于条件生成对抗网络(cGAN)的漫画着色方法，该方法不需要大量训练样本，只需使用一张着色参考图像进行训练。同时，我们还提出了分割和色彩校正方法以缓解出现的模糊和伪像等问题，最终结果清晰、锐利，且保留了原始漫画形象的色彩风格。

使用单个训练图像的基于cGAN的漫画着色