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Quantitative comparison with more methods (on object-shaped holes)

Methods	L1 loss	PSNR	SSIM
FFRN[1]	.0289	25.66	.8787
ContextAttention[2]	.0300	23.73	.8649
FGAware[3]	.0244	26.32	.8811
PConv[4]	.0212	27.57	.8876
PENNet[5]	.0236	26.11	.8845
Ours*	.0194	28.20	.8985
Ours	.0205	27.67	.8949

[1] Guo, Z., Chen, Z., Yu, T., Chen, J., Liu, S.: Progressive image inpainting with full-resolution residual network. In: Proceedings of the 27th ACM International Conference on Multimedia. ACM (2019)

[2] Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., Huang, T.S.: Generative image inpainting with contextual attention. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 5505–5514 (2018)

[3] Xiong, W., Yu, J., Lin, Z., Yang, J., Lu, X., Barnes, C., Luo, J.: Foreground-aware image inpainting. In: IEEE Conference on Computer Vision and Pattern Recognition (2019)

[4] Liu, G., Reda, F.A., Shih, K.J., Wang, T.C., Tao, A., Catanzaro, B.: Image inpainting for irregular holes using partial convolutions. In: European Conference on Computer Vision (2018)

[5] Zeng, Y., Fu, J., Chao, H., Guo, B.: Learning pyramid-context encoder network for high-quality image inpainting. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 1486–1494 (2019)