## 4 Results and Discussions
### Qualitative Comparisons  
During the inpainting task, we show five inpainted frames of the test datasets in Figure 4. The top row is the ground-truth complete frame. The middle row is the result of the method used in [11], while the bottom row is the result of ours. Both [11]'s and our model can successfully inpaint the frame, as shown in (a). However, the method of [11] misses the fence in (d), so obviously our result is much closer to the ground truth, although their result is also acceptable. However, the method proposed in [11] also has the problem that the inpainted hole region has an obvious boundary. We magnify the inpainted holes of (b), (c), and (e) in Figure 5 to compare the details. The red boxes in the image describe the parts that need to be inpainted. In Figure 5a, the boundary of the image of [11] is apparently disharmonious. Our result is much better but a little lacking in texture. In example (b), a profile of the missing region can be clearly observed in the result of [11], and our result has the same problem but looks brighter. As (c) shows, the result of [11] loses some details of the frame, but we restore them successfully. The above results further verify that the non-local mechanism is efficient through learning spatio-temporal features from each frame. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Methods** & **PSNR** & **SSIM** \\ \hline \multirow{6}{*}{BSD100} & Bicubic & 25.96 & 0.6675 \\  & SRGAN & 25.16 & 0.6688 \\  & SRNTT & 24.07 & 0.7290 \\  & RDN & 27.80 & 0.7434 \\  & Ours & 28.41 & 0.7953 \\  & Bicubic & 23.14 & 0.6577 \\  & SRGAN & 22.59 & 0.6780 \\ Urban100 & SRNTT & 25.50 & 0.7830 \\  & RDN & 26.82 & 0.8069 \\  & Ours & 27.25 & 0.8152 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The comparison of SR results between SRGAN, SRNTT, RDN, and our model. In [26], a texture pyramid and approximate nearest neighbor (ANN) search are used to extract patches to restore the hole, but this model is still not as reliable as ours. In our model, we first train the encoder-decoder to ensure that the encoder is able to extract reliable features for reconstruction and the non-local block is able to find useful information from non-local positions. As a result, under the reliable features and non-local information recombination, the following units will go in the right direction. During the SR task, we also conduct qualitative analyses in terms of visual quality and texture enrichment, making comparisons between bicubic interpolation, ground truth (GT), SRGAN [13], SRNTT [14], RDN [15], and our model. The results of two images are shown in Figures 6 and 7 with a scaling factor of \(\times 4\). The corresponding quality metrics of PSNR and SSIM and the mean square error (MSE) belonging to each SR image are also exhibited below. The exhibited images are from BSD100 and Urban100, respectively. Figure 4: Comparative inpainting results on our synthetic test datasets between [11] and our model. Figure 5: Comparison of the inpainted missing part between [11] and our model. As shown in Figures 6 and 7, the red box in the image describe the parts that need to be restored. The compared methods produce blurred edges and noticeable artifacts, while our model achieves higher visual quality by recovering and presenting sharper and clearer edges. In addition, the three quality metrics of two images achieved by our model also outperform the other methods. The reconstructed images generated by our model are more faithful to the GT images, demonstrating that the non-local architecture we adopt is much more efficient by combining non-local information. All these qualitative comparisons of video inpainting and SR tasks further indicate the powerful versatility of our method in completing a variety of tasks, and the results are very competitive. This is of great significance to the development of a general model in the field of video restoration.