End-to-End Rubbing Restoration Using Generative Adversarial Networks

05/08/2022
by   Gongbo Sun, et al.
0

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.

READ FULL TEXT

page 1

page 3

page 7

research
08/11/2019

PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters

Due to the sparsity of features, noise has proven to be a great inhibito...
research
11/17/2017

Chinese Typeface Transformation with Hierarchical Adversarial Network

In this paper, we explore automated typeface generation through image st...
research
02/23/2016

Computer Aided Restoration of Handwritten Character Strokes

This work suggests a new variational approach to the task of computer ai...
research
08/02/2018

Physics-Based Generative Adversarial Models for Image Restoration and Beyond

We present an algorithm to directly solve numerous image restoration pro...
research
12/11/2019

Concept and Experimental Demonstration of Optical IM/DD End-to-End System Optimization using a Generative Model

We perform an experimental end-to-end transceiver optimization via deep ...
research
01/05/2022

Culture-to-Culture Image Translation with Generative Adversarial Networks

This article introduces the concept of image "culturization", i.e., defi...
research
11/03/2022

Shapes2Toon: Generating Cartoon Characters from Simple Geometric Shapes

Cartoons are an important part of our entertainment culture. Though draw...

Please sign up or login with your details

Forgot password? Click here to reset