论文标题
部分可观测时空混沌系统的无模型预测
End-to-End Rubbing Restoration Using Generative Adversarial Networks
论文作者
论文摘要
摩擦修复体对于保护世界文化史至关重要。在本文中，我们提出了用于恢复不完整摩擦字符的Rubbinggan模型。具体来说，我们从张门隆贝伊（Zhang Menglong Bei）收集字符，并建立第一个摩擦修复数据集。我们设计了第一个用于摩擦修复的生成对抗网络。根据我们收集的数据集，我们使用Rubbinggan来学习Zhang Menglong Bei字体样式并恢复角色。实验的结果表明，rubbinggan可以快速有效地修复摩擦摩擦的特征。
Rubbing restorations are significant for preserving world cultural history. In this paper, we propose the RubbingGAN model for restoring incomplete rubbing characters. Specifically, we collect characters from the Zhang Menglong Bei and build up the first rubbing restoration dataset. We design the first generative adversarial network for rubbing restoration. Based on the dataset we collect, we apply the RubbingGAN to learn the Zhang Menglong Bei font style and restore the characters. The results of experiments show that RubbingGAN can repair both slightly and severely incomplete rubbing characters fast and effectively.
