# Video Super-Resolution with Regional Focus for Recurrent Network
## 4 Experiments
### Ablation Experiment  
In order to further explore the influences of different modules of the recurrent network structure proposed in this paper on the final video super-resolution reconstruction results, we designed a network structure containing different modules for ablation experiments to verify the role of each module in the network and its impact on the final reconstruction results. We conducted the ablation experiment on the Vid4 dataset. Firstly, the regional concern block and feature aggregation structure in the network structure were removed, and a recursive network structure containing only 10 residual blocks with series structure was adopted for the super-resolution reconstruction of datasets. Secondly, the feature aggregation structure in the network structure was removed, and the regional focus blocks were reserved. Thirdly, all regional focus blocks in the network structure were removed, and the feature aggregation structure was retained. Finally, the dataset was tested with a complete network structure, including regional focus blocks and feature aggregation structures. The experimental results are shown in Table 1.  
After removing the regional focus blocks and feature aggregation structure, the network structure in this paper degenerates into a recurrent network structure with only concatenated residual blocks. It can be seen from Table 1 that the reconstruction result index of this network is the lowest. In the network that removes the feature aggregation structure and retains the regional focus blocks, the reconstruction results of the video frames are greatly improved. It shows that the regional focus block does pay attention to multiple features of different regions in the video frame, which increases the features diversity involved in reconstruction and improves the quality of the network reconstruction results. In the network that removes the regional focus blocks and retains the feature aggregation structure, the index of the reconstruction results is similar to that of the reconstruction results of that are completely degenerated into a recursive network with only serial residual blocks. This is because the skip connection method of feature aggregation does not completely change the direct relationship between the output of the previous layer and the input of the next layer. Although the shallow features are skip-connected to the last \begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **PSNR** & **SSIM** \\ \hline Ours w/o FA + RF-blocks & 27.69 & 0.848 \\ Ours w/o feature aggregation & 27.91 & 0.849 \\ Ours w/o regional focus blocks & 27.70 & 0.849 \\ Ours & 28.39 & 0.858 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ablation experiments of our method on the Vid4 dataset, by removing different modules in the network and comparing the functions of different modules in the network structure. layer to participate in the reconstruction, the features input to the deep network does not change, and the network can choose only the deep features of the last layer instead of the shallow features during the training process. The network does not use shallow features and degenerates into a series residual network, resulting in similar reconstruction results of the two networks. The last line of Table 1 is the result of the method in this paper. Different features extracted from the regional focus block are skip-connected to the last layer of the network through the feature aggregation structure, and spliced with the deep features to participate in the network reconstruction, and to obtain better reconstruction results. A Reasonable network model structure design can improve the quality of reconstruction results to different degrees.