# Video Super-Resolution with Regional Focus for Recurrent Network
## 4 Experiments
### Ablation Experiment
In addition, simply increasing the depth of the network can expand the receptive field of the convolution kernel, and extract deeper feature information to participate in the reconstruction of the final result, which is helpful to improve the quality of the reconstruction result, but meanwhile, the complexity of the model and the amount of parameters will also increase. In order to explore the influence of model parameters on the reconstruction results, we designed a comparative experiment in Table 2 using Vid4 as the experimental dataset. After removing the regional focus blocks and feature aggregation structures, our method degenerates to a recurrent network structure in which the number of residual blocks is 10, as shown in Table 2. Although the structure has fewer parameters, the reconstruction will result in the lower index. The method marked with '\(*\)' also removes the regional focus block and feature aggregation structure; the difference is that the number of residual blocks is set to 12. This structure increases the depth of the network and the amount of parameters, which brings a small improvement to the quality of the reconstruction results of the model. The last is the reconstruction result of our method that removed the feature aggregation structure. Because of the introduction of regional focus blocks, the model parameters of the network also increase, but they are still less than the parameters amount of the network structure with 12 residual blocks in series. At the same time, the quality of the network reconstruction results is higher than that of the network structure with 12 residual blocks in series. This experiment demonstrates that the proposed regional focus block in the network structure can achieve higher reconstruction benefits with a small increase in the number of model parameters. However, it is not that the larger the number of parameters contained in the network model, the higher the performance of the network model will be. In Table 2, the number of parameters in the second row and the third row differed by 0.1M, but the index of reconstruction results with a large number of parameters are lower. In the method of this paper, we need to balance the relationship between the quantity of network parameters and the quality of network reconstruction. Increasing the number of modules in the network blindly may lead to the opposite results. The network structure of this paper adds a feature aggregation structure on the basis of the regional focus block. This feature aggregation structure does not increase additional parameters, which makes our method obtain great benefits with a small increase in model complexity. The interaction between the regional focus blocks and the feature aggregation structures enriches the diversity of features participating in the reconstruction, and improves the quality of the reconstruction results. \begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Method**} & **Numbers of** & **Numbers of** & & \\  & **Residual Blocks** & **Regional Focus Blocks** & **Param** & **PSNR/SSIM** \\ \hline Ours w/o FA + RF-blocks & 10 & 0 & 3.4M & 27.69/ 0.848 \\ Ours w/o FA + RF-blocks & 12 & 0 & 3.9M & 27.85/ 0.849 \\ Ours w/o Feature Aggregation & 10 & 5 & 3.8M & 27.91/ 0.849 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The influence of the parameters of the network model on the reconstruction results. The results show that the performance of the network model cannot increase indefinitely with the increase in the number of network model parameters. To further demonstrate the role of regional focus blocks in the video frame reconstruction process, we visualize the features extracted from the regional focus blocks. As shown in Figure 7, the green highlighted part is the attention mask learned by the regional focus block. We selected the 'walk' and 'foliage' video sequences from the Vid4 dataset, and the 'caffe' and 'photography' video sequences from the Udm10 dataset.