C3N：Content-Constrained Convolutional Network for Mural Image Completion
Ancient murals, suffering from severe diseases, usually exhibit the absence or distortion of local areas. The damaged murals severely impaired people’s visual appreciation and satisfaction in the digital conservation of cultural heritage. However, there is no large amount of murals due to their scarcity. In this paper, we propose a novel content-constrained convolutional network for mural image completion. This method employs frequency transformation to facilitate effective multi-scale feature fusion for image inpainting, taking into account both space and frequency domains. Our network uses adaptive space-varying activation functions to correct feature maps across scales. Our network also uses dual-domain partial convolution with a mask for computing on only valid points, whereas the mask is updated for the next layer. This iterative process is performed until the mask is filled to build the repaired image. The proposed method is verified on the datasets in comparison with baseline methods. The experimental results demonstrate that the proposed method achieves better results with less artifacts in repairing mural images and generally outperforms the state-of-the-art methods both quantitatively and qualitatively. The code and pretrained models are available at https://github.com/zhangyongqin/C3N.
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