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End-to-End Rubbing Restoration Using Generative Adversarial Networks

2022-05-08 00:09:50
Gongbo Sun, Zijie Zheng, Ming Zhang
arXiv_AI
         
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2205.03743

PDF

https://arxiv.org/pdf/2205.03743.pdf

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