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Structure-guided Generative Adversarial Network for Image Inpainting

Huan Liang

Huan Liang

School of Computer Science and Engineering, Xi'an Technological UniversityXi'an, China
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Liang, Huan
, 
Li Zhao

Li Zhao

School of Computer Science and Engineering Xi'an Technological UniversityXi'an, China
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Zhao, Li
 and 
Lei Cao

Lei Cao

School of Computer Science and Engineering, Xi'an Technological UniversityXi'an, China
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Cao, Lei
  
Dec 31, 2024
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Published Online: Dec 31, 2024
Page range: 1 - 8
DOI: https://doi.org/10.2478/ijanmc-2024-0031
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© 2024 Huan Liang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Overall structure of the image inpainting network
Overall structure of the image inpainting network

Figure 2.

Schematic diagram of gated convolution structure
Schematic diagram of gated convolution structure

Figure 3.

Curves of Loss Functions during Model Training
Curves of Loss Functions during Model Training

Figure 4.

A partial sample of the Places2 dataset
A partial sample of the Places2 dataset

Figure 5.

A partial sample of the CelebA dataset
A partial sample of the CelebA dataset

Figure 6.

A partial sample of the Irregular mask dataset
A partial sample of the Irregular mask dataset

Figure 7.

The repair effect of each algorithm is displayed
The repair effect of each algorithm is displayed

Figure 8.

Comparison of Inpainting Results at Different Iterations during Training
Comparison of Inpainting Results at Different Iterations during Training

PSNR/SSIM for different image inpainting methods and different mask area ratios on the places2 dataset

Mask Ratio PSNR/SSIM
CE Pconv EC Ours
1%-10% 29.26/0.937 30.87/0.929 32.58/0.947 33.89/0.961
10%-20% 21.34/0.746 24.62/0.887 27.15/0.916 28.43/0.935
20%-30% 19.58/0.658 21.43/0.824 24.33/0.859 25.58/0.878
30%-40% 17.82/0.549 19.32/0.751 23.17/0.782 23.81/0.814
40%-50% 15.77/0.475 17.48/0.682 21.64/0.747 22.04/0.763
50%-60% 14.25/0.416 16.44/0.613 19.46/0.651 20.53/0.686
Language:
English
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Journal Subjects:
Computer Sciences, Computer Sciences, other
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