Computer Science ›› 2022, Vol. 49 ›› Issue (6): 217-223.doi: 10.11896/jsjkx.210500105

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Digital Mural Inpainting Method Based on Feature Perception

XU Hui1,2, KANG Jin-meng1, ZHANG Jia-wan1   

  1. 1 School of Computer Software,Tianjin University,Tianjin 300072,China
    2 Digitization Technology Research Center for Cultural Heritage Conservation and Promotion,Henan Institute of Science and Technology,Xinxiang,Henan 453000,China
  • Received:2021-05-14 Revised:2021-07-20 Online:2022-06-15 Published:2022-06-08
  • About author:XU Hui,born in 1986,Ph.D,is a member of China Computer Federation.Her main research interests include image synthesis and digital conservation of cultural heritage.
    ZHANG Jia-wan,born in 1975,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include image synthesis,visualization and visual analysis.
  • Supported by:
    National Key R & D Program of China(2019YFC1521200).

Abstract: There are irregular damaged areas caused by various diseases of grottoes in Dunhuang murals,digital restoration is used to restore the image of the Dunhuang grotto murals,which will not cause damage to the original murals,but also get a better repair effect.Because of the large missing area in the mural mending,it cannot be realized by local non-semantic repair methods.Aiming at the restoration of the defective area of Dunhuang grotto murals,this paper designs an image repair method based on the generation of confrontation network,and uses semantically reasonable content to render the pixels in the missing area to realize the reconstruction of non-contact mural scenes,improve the efficiency of mural virtual restoration and the accuracy of restoration.The algorithm introduces a perceptual-loss function on the basis of generating an adversarial neural network,adds a three-layer convolutional layer to the generation model to collect image features of damaged areas,uses the perceptual loss to improve the model’s ability to repair high-frequency texture details,and uses extended convolution to extract range features,so as to stimulate the generative model to generate higher quality image results.Compared with three excellent methods on the Dunhuang grotto mural data set,and the repair results show that the PSNR score of the proposed algorithm on the test data set increases by 1.79%,and the SSIM score increases by 7.7%.The proposed repair model improves the repair accuracy of damaged murals and makes the repair results more accurate.

Key words: Expansion convolution, Generative adversarial networks, Mural inpainting, Perceptual loss

CLC Number: 

  • TP181
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