Ga-RFR: Recurrent Feature Reasoning with Gated Convolution for Chinese Inscriptions Image Inpainting
Authors: 
Long Zhao, Yuhao Lou, Zonglong Yuan, Xiangjun Dong, Xiaoqiang Ren, Hongjiao GuanAuthors Info & Claims
Pages 320 - 331
Abstract
Inscriptions were a primary means of recording historical events and literary works in ancient times, and remain an important part of Chinese ancient civilization. However, due to their large quantity and prolonged exposure to the natural environment, inscriptions have suffered significant damage. Traditional manual restoration methods are time-consuming and labor-intensive, making it necessary to explore new restoration techniques. In this study, we introduce a Ga-RFR network that uses gated convolution to replace ordinary convolution. This technique reduces feature redundancy in generated feature maps and minimizes the production of unnecessary information, thereby enhancing restoration effectiveness. We also compare our method with other advanced image restoration algorithms, and our results demonstrate that our approach outperforms other current methods.
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Information
Published In
Sep 2023
625 pages
ISBN:978-3-031-44209-4
DOI:10.1007/978-3-031-44210-0
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 26 September 2023
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