EDU-GAN:Edge Enhancement Generative Adversarial Networks with Dual-Domain Discriminators for Inscription Images Denoising  

作　　者：Yunjing Liu Erhu Zhang Jingjing Wang Guangfeng Lin Jinghong Duan 

机构地区：[1]School of Mechanical and Precision Instrument Engineering,Xi’an University of Technology,Xi’an,710048,China [2]Department of Information Science,Xi’an University of Technology,Xi’an,710054,China [3]School of Faculty of Painting,Packaging Engineering and Digital Media,Xi’an University of Technology,Xi’an,710048,China [4]School of Computer Science and Engineering,Xi’an University of Technology,Xi’an,710048,China

出　　处：《Computers, Materials & Continua》2024年第7期1633-1653,共21页计算机、材料和连续体（英文）

基　　金：supported by the Key R&D Program of Shaanxi Province,China(Grant Nos.2022GY-274,2023-YBSF-505);the National Natural Science Foundation of China(Grant No.62273273).

摘　　要：Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research issue.Different fromnatural images,character images pay more attention to stroke information.However,existingmodelsmainly consider pixel-level informationwhile ignoring structural information of the character,such as its edge and glyph,resulting in reconstructed images with mottled local structure and character damage.To solve these problems,we propose a novel generative adversarial network(GAN)framework based on an edge-guided generator and a discriminator constructed by a dual-domain U-Net framework,i.e.,EDU-GAN.Unlike existing frameworks,the generator introduces the edge extractionmodule,guiding it into the denoising process through the attention mechanism,which maintains the edge detail of the restored inscription image.Moreover,a dual-domain U-Net-based discriminator is proposed to learn the global and local discrepancy between the denoised and the label images in both image and morphological domains,which is helpful to blind denoising tasks.The proposed dual-domain discriminator and generator for adversarial training can reduce local artifacts and keep the denoised character structure intact.Due to the lack of a real-inscription image,we built the real-inscription dataset to provide an effective benchmark for studying inscription image denoising.The experimental results show the superiority of our method both in the synthetic and real-inscription datasets.

关 键 词：Dual-domain discriminators inscription images DENOISING edge-guided generator 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]