# Structure-Guided Image Inpainting Based on Multi-Scale Attention Pyramid Network
## 4 Experimental Results and Datasets
### Qualitative Comparison  
In image inpainting tasks, compared to achieving high scores on various metrics, the visual quality and authenticity of the restoration results are more crucial. Given that image inpainting currently cannot fully restore areas with extensive damage, it is essential that the filled content in these missing areas appears as visually authentic and coherent as possible. Therefore, image inpainting often requires qualitative assessment. Figures 5 and 6 present the experimental results of the image inpainting method proposed \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{3}{c}{**Methods**} \\ \cline{3-8}  & & **FFTI [44]** & **SWT [45]** & **FMGDN [46]** & **MFMAM [3]** & **Proposed** \\ \hline \multirow{4}{*}{**PSNR\(\uparrow\)**} & (0.1,0.2] & 29.1022 & 29.6467 & 30.0476 & 29.9150 & **30.9707** \\  & (0.2,0.3] & 26.0733 & 26.1348 & 26.7547 & 27.2925 & **27.5798** \\  & (0.3,0.4] & 24.6046 & 24.2154 & 24.2957 & 25.4415 & **25.4962** \\  & (0.4,0.5] & 23.1416 & 22.6202 & 22.5091 & **24.0152** & 23.7441 \\  & (0.5,0.6] & 20.7836 & 20.3978 & 20.5042 & 21.3015 & **21.5946** \\ \hline \multirow{4}{*}{**SSIM\(\uparrow\)**} & (0.1,0.2] & 0.9289 & 0.9424 & **0.9505** & 0.9434 & 0.9489 \\  & (0.2,0.3] & 0.8875 & 0.8916 & 0.8934 & 0.9083 & **0.9149** \\  & (0.3,0.4] & 0.8437 & 0.8369 & 0.8527 & 0.8735 & **0.8809** \\  & (0.4,0.5] & 0.7932 & 0.7767 & 0.8032 & 0.8351 & **0.8416** \\  & (0.5,0.6] & 0.6769 & 0.6664 & 0.6937 & 0.7213 & **0.7280** \\ \hline \multirow{4}{*}{**MAE\(\downarrow\)**} & (0.1,0.2] & 0.0144 & 0.0126 & 0.0118 & 0.0238 & **0.0094** \\  & (0.2,0.3] & 0.0245 & 0.0233 & 0.0215 & 0.0296 & **0.0188** \\  & (0.3,0.4] & 0.0345 & 0.0344 & 0.0319 & 0.0423 & **0.0266** \\  & (0.4,0.5] & 0.0453 & 0.0476 & 0.0438 & 0.0563 & **0.0364** \\  & (0.5,0.6] & 0.0647 & 0.0664 & 0.0633 & 0.0738 & **0.0548** \\ \hline \multirow{4}{*}{**FID\(\downarrow\)**} & (0.1,0.2] & 25,0562 & 25.1856 & **14.5851** & 18.5755 & 15.0556 \\  & (0.2,0.3] & 53.5997 & 53.9972 & 42.0211 & 39.6055 & **28.666** \\  & (0.3,0.4] & 63.9667 & 81.1525 & 53.7013 & 48.0577 & **42.3663** \\  & (0.4,0.5] & 87.0932 & 103.0111 & 49.9758 & 54.4916 & **53.8751** \\  & (0.5,0.6] & 106.2307 & 135.0156 & 76.4924 & 80.6035 & **73.1309** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: These are the experimental data of the proposed image inpainting method in this paper with several other image inpainting methods on the Paris Street View dataset, where the second column represents the percentage of damaged area in the damaged image. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{3}{c}{**Methods**} \\ \cline{3-8}  & & **FFTI [44]** & **SWT [45]** & **FMGDN [46]** & **MFMAM [3]** & **Proposed** \\ \hline \multirow{4}{*}{**PSNR\(\uparrow\)**} & (0.1,0.2] & 29.1628 & 29.7304 & 30.1550 & 29.9982 & **31.0290** \\  & (0.2,0.3] & 26.1752 & 26.3189 & 26.9222 & 27.4576 & **28.4681** \\  & (0.3,0.4] & 24.7306 & 24.4325 & 24.5076 & 25.2564 & **25.8856** \\  & (0.4,0.5] & 23.3957 & 22.8416 & 22.7389 & **2  
in this paper, as well as several other advanced methods, on the Paris Street View and CelebA datasets, respectively. Observations from the Paris Street View dataset reveal that our structure-assisted guidance repair method can restore more structural details compared to other methods while ensuring the accuracy of the predicted structures. This is due to our proposed dual U-Net constrained edge prediction network, which can provide more accurate constraints during training to enhance the prediction capability of the edge prediction branch. During the subsequent stage of image texture and semantic content prediction, this structural guidance allows the network to generate images with more accurate lines and fewer structural distortions, even when most of the image is missing. The experimental results also confirm our method's significant advantages in handling structurally dense image inpainting tasks.