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Table 2 Quantitative comparison results of different methods for restoring inexistent texts in the training dataset (“↑” indicates that higher is better. “↓” indicates that lower is better. The best results are marked in bold black)

From: EA-GAN: restoration of text in ancient Chinese books based on an example attention generative adversarial network

Method

PSNR↑

SSIM↑

LPIPS(AlexNet)↓

LPIPS(VGG)↓

GLCIC

21.93

0.905

0.0458

0.0736

PConv

22.22

0.900

0.0502

0.0785

GatedConv

22.19

0.901

0.0482

0.0742

EdgeConnect

23.34

0.917

0.0441

0.0674

RFR-net

22.99

0.911

0.0467

0.0659

Ours

24.66

0.925

0.0267

0.0559