# Fragments Inpainting for Tomb Murals using a Dual-Attention Mechanism GAN with Improved Generators
## 4 Experiments
### Comparison Experiments  
This section compares the model proposed in this paper with some mainstream inpainting models. The main models compared are coherent semantic attention for image inpainting (CSA) [17], recurrent feature reasoning net (RFR) [18], resolution-robust large mask inpainting with Fourier convolutions (LaMa) [20], and image inpainting via conditional texture and structure dual-generation (CTSDG) [19]. The experimental mask is the same as the test mask, and different types of masks are used for inpainting images. The specific inpainting results are shown in Figures 14-17 below. model will not produce textures that do not conform to the image information. Due to the small number of datasets and sparse features, the inpainting is imperfect for the random large mask. This network can better represent the image information for the mural dataset and shows precise details. The details are shown below. The comparison in Figure 14 shows that when the CSA model is used, the resulting trunk is thicker in the lower right corner of the image in the second row at the green box compared to the original image, which repairs the image but does not accurately inpaint the image. The third row of the image has a blurred texture in the fold in the top left corner of the red box. When the RFR model was used, more obvious artifacts were produced. When using the LaMa model, the internal texture of the stone is missing in the red box in the bottom right corner of the first row of images. The repaired tree trunk in the green box in the bottom right corner of the second row of images needs to be structurally sound, with the trunk shape not matching the original image. When using the CTSDG model, artifacts are present, and in both the top left red box and the bottom correct green box in the second row, there are blurred details. With the model in this paper, there are fewer artifacts, and the detail recovery is more similar to the original image in both the red box in the bottom right corner of the first row and the green box in the bottom right corner of the second row. In summary, our network generates fewer image artifacts on the epitaxial mask and produces a structure and texture similar to the original image. The comparison in Figure 15 shows that the inpainting results are similar across the networks on the crack masks, with our network generating relatively good images when the difference is not significant. When the CSA model is used, the inpainting results are similar to the original image. With the RFR model, the inpainting of the leaves in the first row of the image in the red box is blurred, and the details are not precise. With the RFR model, the artifacts in the red box in the third row are more prominent. With the LaMa model, the inpainting is similar to the original image. When using the CTSDG model, the inpainting of the leaves and trunk in the first row of the image is blurred and lacks detail. Our model's overall inpainting is better, and the detail repaired at the red box in the first row is more similar to the original image. In summary, our network can produce a structure and texture more similar to the original image on the crack mask. Figure 15: Comparison of inpainting results for each model on the crack masks. Our model works best and looks more similar to the original after the comparison. The comparison in Figure 16 shows that our image has fewer artifacts on the small mask, and the image is more similar to the original. With the CSA model, the image is repaired at the red box in the first row and the red box in the second row, but the inpainting structure does not resemble the original image structure. With the RFR model, artifacts were more evident in the second and third rows of the image at the red box, and the defect at the trunk was not repaired. With the LaMa model, the inpainting of the trunk in the red box in the first row of the image is different from the original structure, and the inpainting of the red box in the third row is reddish and has a color different from the original image.