# Video Super-Resolution with Regional Focus for Recurrent Network
## 3 Proposed Model
### Network Structure  
The network structure designed in this paper mainly consists of three parts, which are the positive feedback part as the input, the basic module part for feature regional focus and multi-feature fusion, and the output part for updating and reconstructing the hidden state. Their structure relationship is shown in Figure 2.  
The leftmost part of Figure 2 is the input part of the network; in the input part of the network, the adjacent LR frames \(x_{t-1}\) and \(x_{t}\), the reconstruction result \(y_{t-1}\) of the previous frame along the channel axis and the hidden state \(h_{t-1}\) generated in the reconstruction process are spliced, and then sent to the network. For the video super-resolution task, if the temporal continuity of the network model is limited, the reconstruction results are prone to flicker distortion. The hidden state is spliced with the adjacent LR frames and fed to the network, which helps to improve the continuity between frames and reduce the occurrence of flicker and distortion. The reconstruction result \(y_{t-1}\) of the previous frame is not directly spliced, but is obtained at the same size as the input frame after the space-to-depth transformation, and then spliced as the network input. Due to the high correlation between adjacent frames, when the reconstruction result \(y_{t}\) is generated, the reconstruction result of the previous frame contains the additional texture information that has been processed at the previous time, which can supplement the information of the similar part of the current frame. In addition, the preceding LR frame \(x_{t-1}\) is input into the network along with the co responding rebuild output \(y_{t-1}\). This input-output mapping relationship can guide the content that needs to be learned for the current frame reconstruction. As shown in Figure 2, then 3 \(\times\) 3 convolution is used to extract features from the input network concatenation information, and the extracted features are sent into the BM module. The BM module consists of two residual blocks and one regional focus block. The residual block here can extract high-frequency information contained in the feature information, and the regional focus block can decouple from the next BM module and focus on areas that are difficult to recover in the video frame. Through the feature aggregation structure, the high-frequency feature information extracted from different BM modules and the regional feature information of focus are sent to the last layer of the network for concatenation. After the feature fusion of \(1\times 1\) convolution, the feature information is sent to the two outputs of the network, respectively. One channel is directly output to hidden state \(h_{t}\) after \(3\times 3\) convolutions. In other channel, the result after a depth-to-space transformation is added with the up-sampling result of the LR frame \(x_{t}\) after bicubic interpolation as the super-resolution output of the current frame \(y_{t}\). The LR frame \(x_{t}\) contains the profile information of the frame to be reconstructed. The interpolation and up-sampling of this frame is to reduce the network's estimation of the profile information, put more computing resources into the recovery of details, and reduce the network's computation. When the reconstruction result \(y_{t-1}\) of the previous frame is sent to the network as positive feedback to participate in the reconstruction of the next frame, a space-to-depth transformation needs to be performed to match the size of the input low-resolution frame. The output part of the network is transformed from depth to space. After transformation, all pixels contained in the low-resolution feature are rearranged in the corresponding high-resolution feature area, preserving local integrity. The depth-to-space and space-to-depth transformations are inverse to each other.