[1] Deng Q Y. The multi-culture heritage value of Chinese traditional embroidery craftsmanship[J]. Journal of Chongqing Three Gorges University, 34, 39-49(2018).

[2] Huang H X, Wang Z L. Artistic value and aesthetic significance of the T-shaped silks painting of Mawangdui tombs of Han dynasty[J]. Journal of Hebei University of Science and Technology (Social Sciences), 22, 59-63(2022).

[3] Goodfellow I, Pouget-Abadie J, Mirza M et al. Generative adversarial nets[C], 2672-2680(2014).

[4] Mirza M, Osindero S. Conditional generative adversarial nets[EB/OL]. https://arxiv.org/abs/1411.1784

[5] Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[EB/OL]. https://arxiv.org/abs/1511.06434

[6] Zhang H, Xu T, Li H S et al. StackGAN: text to photo-realistic image synthesis with stacked generative adversarial networks[C], 5908-5916(2017).

[7] Pathak D, Krähenbühl P, Donahue J et al. Context encoders: feature learning by inpainting[C], 2536-2544(2016).

[8] Iizuka S, Simo-Serra E, Ishikawa H. Globally and locally consistent image completion[J]. ACM Transactions on Graphics, 36, 1-14(2017).

[9] Yu J H, Lin Z, Yang J M et al. Generative image inpainting with contextual attention[C], 5505-5514(2018).

[10] Yu J H, Lin Z, Yang J M et al. Free-form image inpainting with gated convolution[C], 4470-4479(2020).

[11] Nazeri K, Ng E, Joseph T et al. EdgeConnect: generative image inpainting with adversarial edge learning[EB/OL]. https://arxiv.org/abs/1901.00212

[12] Liu Q, Zhang D C. Dense convolution generative adversarial network image inpainting method with SENet[J]. Journal of Chinese Computer Systems, 43, 1056-1060(2022).

[13] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).

[14] Gao W C, Ren S B, Tian C et al. Research on method of animated avatar generation based on multi-level generative adversarial networks[J]. Computer Engineering and Applications, 58, 230-237(2022).

[15] Luo D L, Jiang M, Yuan L J et al. Research on image coloring based on conditional generative adversarial network[J]. Computer Engineering and Applications, 57, 193-198(2021).

[16] Hou X D, Liu H R, Liu H P. High-resolution damaged images restoration based on convolutional auto-encoder generative adversarial network[J]. Journal of Image and Graphics, 27, 1645-1656(2022).

[17] Li H Y, Wu Z Y, Guo L et al. Multi-discriminator image inpainting algorithm based on hybrid dilated convolution network[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 49, 40-45(2021).

[18] Chen Z N, Zhang H Y, Zeng N Y et al. Attention mechanism embedded multi-scale restoration method for blurred image[J]. Journal of Image and Graphics, 27, 1682-1696(2022).

[19] Li Y L, Gao Y, Yan J L et al. Image inpainting methods based on deep neural networks: a review[J]. Chinese Journal of Computers, 44, 2295-2316(2021).

[20] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018, 11211, 3-19(2018).

[21] Huang Y W, Tang X, Zhou B. Image super-resolution reconstruction network with dual attention and structural similarity measure[J]. Chinese Journal of Liquid Crystals and Displays, 37, 367-375(2022).

[22] Huang Y W, Tang X, Zhou B. Image super-resolution reconstruction network with dual attention and structural similarity measure[J]. Chinese Journal of Liquid Crystals and Displays, 37, 367-375(2022).

[23] Wang P Q, Chen P F, Yuan Y et al. Understanding convolution for semantic segmentation[C], 1451-1460(2018).

[24] Zhou Z Y, Wang H, Zhang X Q et al. Classification model for Chinese traditional embroidery based on Xception-TD[J]. Data Analysis and Knowledge Discovery, 6, 338-347(2022).

[25] Liu J. Research and implementation of thang-ga image inpainting system based on deep learning[D](2020).

[26] Wang Z, Bovik A C, Sheikh H R et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 13, 600-612(2004).

[27] Kang J M. Research on murals inpainting based on generative adversarial networks[D](2019).

[28] Razavian A S, Azizpour H, Sullivan J et al. CNN features off-the-shelf: an astounding baseline for recognition[C], 512-519(2014).