关键词: 人像着色, 联合一致循环生成对抗网络, 深度学习, 特征融合

Abstract:

Traditional grayscale image coloring methods have problems such as color distortion and poor effect, and have been gradually replaced by deep learning methods. At present, the portrait coloring method based on deep learning mainly has the problem of portrait miscoloring in complex background. In view of the above problems, this paper proposes a portrait coloring method for joint consistent cyclic generative adversarial network. The method uses a joint consistency loss training model based on the cycle generative adversarial network. The generative network uses the U-network structure （UNet） to improve the model generative network and enhance the integrity of the generated image information. The feature extraction method of multi-feature fusion is introduced in the adversarial network, and the feature expression of the feature is enhanced. Finally, through the comparison experiments in the self-built CASIA-PlusColors high-quality portrait dataset, it is verified that the proposed method has a better effect on portrait coloring in complex background.

Key words: portrait coloring, joint consistent cycle generative adversarial network, deep learning, feature fusion