# Video Super-Resolution with Regional Focus for Recurrent Network
## 3 Proposed Model
### Feature Aggregation  
The results of the regional focus block are sent to the lower BM module as the input features. This serial structure determines that the features participating in the final reconstruction results are deep-seated features, while the shallow features can not effectively participate in the network reconstruction, thus reducing the learning efficiency of the network. Therefore, we have designed a skip connection structure in the network, which connects the shallow features of the regional focus block output to the last layer of the network. In addition to the serial structure, there is also a parallel structure between the features of each layer, which sends the features of different regional focus blocks to the last layer of the network for splicing with the deep features. The dimension of splicing results is reduced through \(1\times 1\) convolutions, and the aggregation features obtained can be used to reconstruct the final result and update the hidden state. We call this structure Feature Aggregation (FA), as shown in Figure 6.  
We use the calculation Formula (4) to express the feature aggregation calculation process in Figure 6, as follows:  
\[F_{s}=Conv_{1\times 1}\{Concat\{F_{1},F_{2},F_{3},F_{4},F_{5}\} \tag{4}\]  
where \(F_{1},F_{2},F_{3},F_{4},F_{5}\) represents the output characteristics of different BMs. \(Concat\{\cdot\}\) represents the splicing operations. \(Conv_{1\times 1}\{\cdot\}\) represents the fuse features with \(1\times 1\) convolution kernels. \(F_{s}\) represents the output features, which participate in the final result reconstruction and the hidden state update.  
By splicing the shallow features and deep features via skip connection, the number of features involved in video frame reconstruction can be increased without increasing the network parameters. There are \(n\) BMs in the network, and the number of features is expanded by \(N\) times. The number of BMs is set to 5 in our network. The feature  
Figure 5: The structure of the regional focus block (RF-Block).  
aggregation structure takes into account the diversity of features while retaining the shallow features to participate in network reconstruction. In the process of network reconstruction, the shallow network mainly focuses on the simple areas and smooth areas of the video frame to be reconstructed, and the deep network mainly focuses on the complex areas and texture areas of the video frame to be reconstructed.  
Feature aggregation is essentially a feature selection process. Under the function of a regional focus block, the features of different layers focus on the regions with different reconstruction difficulties of the video frame. The skip connection structure sends the different features output from different BMs to the last layer of the network, and then splices them for convolution and fusion, so as to improve the availability of parameters. During network training, the loss between the reconstruction results and the label frames directly affects each regional focus block through the skip connection structure of feature aggregation. The regions with poor reconstruction results can be directly fed back to the regional focus block, which can be optimized in the subsequent network, thereby obtaining better reconstruction results.  
Finally, the aggregation features that contain rich feature information are divided into two ways. One is the convolution operation, depth-to-space transformation, and the interpolated up-sampled frame to be reconstructed, which is added as the reconstruction result \(y_{t}\) of the current frame output network. The others is the result with convolution and nonlinear operation as the hidden state \(h_{t}\) output network. Then, the reconstruction result \(y_{t}\), the hidden state \(h_{t}\), the next frame to be reconstructed and the preamble frame of this frame are sent to the network together to start the reconstruction of the next frame, and the process is repeated until the reconstruction of all video frames is completed.