# Fragments Inpainting for Tomb Murals using a Dual-Attention Mechanism GAN with Improved Generators
## 5 Conclusions  
In order to reduce costs and assist in manual inpainting, we conducted this study. In this paper, we proposed an improved generative adversarial network of fragmentation of tomb murals. The protection of the image structure was enhanced by improving the generator to include prior knowledge and channel attention. The inpainting accuracy of the model was improved by using a new module of feature aggregation, which combines multiscale feature aggregation with a dual-attention mechanism and switches to depth-separable convolution. A joint loss function was used to improve the inpainting results further. The improved model improved the ability to accurately inpaint the original murals, satisfying the requirement of \"inpainting the old to the old\" as far as possible. Our model showed an overall improvement in the effectiveness of inpainting different disease types. Objectively, on the epitaxial mask, PSNR increased by 0.5128, and MSE decreased by 0.0024. On the crack mask, PSNR increased by 1.0458. On the random small mask, PSNR increased by 3.4319, SSIM increased by 0.0059, and MSE decreased by 0.0012. On the random large mask, PSNR increased by 0.2812, SSIM increased by 0.0005, and MSE decreased by 0.0003. In terms of objective metrics, the largest improvement was seen in PSNR. In terms of objective metrics, the most remarkable improvement was seen in PSNR. In aggregate, the greatest numerical improvement was seen in random small masks, followed by random \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mask Type** & **Method** & **Ours** & **CSA** & **RFR** & **LaMa** & **CTSDG** \\ \hline \multirow{3}{*}{epitaxial mask} & PSNR & **25.2366** & 25.0880 & 23.9347 & 25.1932 & 24.7238 \\  & SSIM & 0.8509 & 0.7990 & 0.8108 & 0.8468 & **0.8524** \\  & MSE & 0.0062 & 0.0053 & 0.0067 & **0.0050** & 0.0086 \\ \hline \multirow{3}{*}{crack mask} & PSNR & **31.8855** & 28.6886 & 31.1276 & 31.2935 & 30.8397 \\  & SSIM & 0.9132 & 0.8262 & 0.9102 & 0.9114 & **0.9150** \\  & MSE & 0.0025 & 0.0035 & 0.0030 & **0.0019** & 0.0022 \\ \hline \multirow{3}{*}{small mask} & PSNR & **35.6748** & 28.9423 & 34.4726 & 35.1649 & 32.2429 \\  & SSIM & **0.9597** & 0.8491 & 0.9562 & 0.9550 & 0.9538 \\  & MSE & **0.0008** & 0.0032 & 0.0010 & 0.0009 & 0.0020 \\ \hline \multirow{3}{*}{large mask} & PSNR & **25.2643** & 23.2033 & 23.5682 & 25.0319 & 24.9831 \\  & SSIM & 0.7311 & **0.7323** & 0.7201 & 0.7124 & 0.7306 \\ \cline{1-1}  & MSE & **0.0077** & 0.0094 & 0.0113 & 0.0091 & 0.0080 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative experimental test indicators for each model. large masks. From a subjective point of view, the graphs we generated were also found by experts to be more compatible with the inpainting. The results showed that our method can effectively address three types of diseases to tomb murals: long-term environmental erosion and human damage; fissures in the murals and large hollow drums in the floor of the tomb murals lead to the loss of the floor layer and missing images as well as artificial excavation. In the future, we will further address the limitations of our model and will continue to improve the quality of the inpainting model of diversified mural deterioration. As the number of tomb murals is small and precious, we will continue to collect more tomb mural images to build a larger dataset to enable the model to learn and inpaint better. Conceptualization, M.W. ; methodology, M.W. ; writing--review and editing, M.W. ; software, X.C. ; writing--original draft preparation, X.C. ; validation, J.W. ; resources, J.W. ; data curation, J.W. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (No. 61701388), the Interdisciplinary Foundation of Xi'an University of Architecture and Technology (No. X20220082), and the Natural Science Foundation of Xi'an University of Architecture and Technology (No. ZR21032). The Mural dataset presented in this study is available from the Shaanxi History Museum.