A restoration method using dual generate adversarial networks for Chinese ancient characters  

作　　者：Benpeng Su Xuxing Liu Weize Gao Ye Yang Shanxiong Chen 

机构地区：[1]College of Computer and Information Science,Southwest University,Chongqing 400715,China [2]Chongqing Key Lab of Automated Reasoning and Cognition,Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences,Chongqing 400714,China

出　　处：《Visual Informatics》2022年第1期26-34,共9页可视信息学（英文）

基　　金：the National Social Science Foundation of China under Grant 19BYY171;the Fundamental Research Funds for the Central Universities under Grant XDJK2013C117;Ph.D.Fund of Southwest University No.20130553;China Postdoctoral Science Foundation under Grant 2015M580765;Chongqing Postdoctoral Science Foundation under Grant Xm2016041;Chongqing Natural Science Foundation(cstc2019jcyj-msxmX0130);Chongqing City science and technology education research projects(KJQN201801901).

摘　　要：Ancient books that record the history of different periods are precious for human civilization.But the protection of them is facing serious problems such as aging.It is significant to repair the damaged characters in ancient books and restore their original textures.The requirement of the restoration of the damaged character is keeping the stroke shape correct and the font style consistent.In order to solve these problems,this paper proposes a new restoration method based on generative adversarial networks.We use the shape restoration network to complete the stroke shape recovery and the font style recovery.The texture repair network is responsible for reconstructing texture details.In order to improve the accuracy of the generator in the shape restoration network,we use the adversarial feature loss(AFL),which can update the generator and discriminator synchronously to replace the traditional perceptual loss.Meanwhile,the font style loss is proposed to maintain the stylistic consistency for the whole character.Our model is evaluated on the datasets Yi and Qing,and shows that it outperforms current state-of-the-art techniques quantitatively and qualitatively.In particular,the Structural Similarity has increased by 8.0%and 6.7%respectively on the two datasets.

关 键 词：Stroke shape restoration Texture restoration Calligraphy style 

分 类 号：R74[医药卫生—神经病学与精神病学]