# Video Super-Resolution with Regional Focus for Recurrent Network
## 3 Proposed Model
### Residual Calculation and Regional Focus in the BM Module  
After the different information input to the network is spliced, the convolution operation is first performed to extract different feature information, and then the information is sent to the BM basic module of the network. Each BM base module in the designed network structure contains two residual blocks and one regional focus block, as shown in Figure 4. The cascade of residual blocks can transfer rich image information from the previous layer to the next layer. The regional focus block (RF-Block) can adaptively focus on the features of specific regions, and extract the feature information that needs to be focused on in different regions. The calculation process of the first BM block in Figure 4 can be described by mathematical Formula (2); the calculation of other BM blocks is the same as this process:  
\[F_{1}=RF\{Res\{Res\{F_{0}\}\}\} \tag{2}\]  
where \(F_{0}\) is the input feature, \(Res\{\cdot\}\) is the residual feature calculation, \(RF\{\cdot\}\) is the regional focus feature calculation, and \(F_{1}\) is the output feature of the first BM block. After all BM blocks are calculated, the output feature \(F_{i}\) is obtained to participate in the subsequent work of the network. The cascade of residual blocks can extract the feature information contained in the image. With the increase in network layers, the deep feature information of the input information can be further extracted, and the skip connections of residual blocks can alleviate the gradient disappearance problem with the increase in network depth. The task of video super-resolution is to restore the missing information to the complete information. In the traditional network structure, the information will be lost in the transmission process, which will affect the reconstruction quality. The skip connection structure can ensure the integrity of information transmission in the network and avoid information loss. The main task of video super-resolution reconstruction is to minimize the pixel-by-pixel error between the reconstruction result and the high-resolution label. Video frames contain smooth regions with single details, and complex regions with rich textures. In the reconstruction process of the video frame, the loss values of smooth regions and label frames are small, but the loss values of complex regions and label frames are quite different. Compared with complex regions, smooth regions account for a larger proportion in video frames. This data imbalance leads to the optimization direction of the network being dominated by smooth regions, resulting in the unsatisfactory reconstruction results of complex regions. Therefore, we add regional focus blocks to the BM. During the network training process, the regional focus module is used to focus on different regions in the process of video frame reconstruction, and not just simple regions. The structure of the regional focus block is shown in Figure 5. After the feature \(F_{n}\) is input into the regional focus block, step convolution is first used to reduce the dimension of  
Figure 4: The structure of the base module (BM). space and channel for the feature \(F_{n}\). The down-samplings of space and channels ensures the lightweight nature of the module. Meanwhile, not all features require a regional focus. The dimensionality reduction for feature channels can adaptively select the feature channels that need to be focused. Then, the large receptive field is obtained by pooling the large-size max, and the significant features in the space are obtained by connecting the convolution layer. The significant features are restored to the input size using bilinear interpolation and \(1\times 1\) convolution, and the regional focus weights are obtained by the sigmoid function. The result of multiplying the weight by the input feature \(F_{n}\) is spliced with the input feature \(F_{n}\) in the channel dimension, and the new feature \(F_{m}\) after regional focus is obtained through \(1\times 1\) convolution fusion.