End-to-End Rubbing Restoration Using Generative Adversarial Networks
This work addresses the preservation of cultural history through rubbing restoration, but it is incremental as it applies an existing GAN method to a new domain-specific dataset.
The paper tackled the problem of restoring incomplete rubbing characters by proposing RubbingGAN, a generative adversarial network, which effectively repaired both slightly and severely damaged characters, as demonstrated on a dataset from the Zhang Menglong Bei.
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.