# Video Super-Resolution with Regional Focus for Recurrent Network
## 4 Experiments
### Comparative Experiment  
We test our proposed method on the datasets Vid4 and Udm10, and compare them with the existing video super-resolution methods TOFLOW [6], FRVSR [4], DUF [9], RBPN [8], RLSP [7], PFNL [11], and RRN [19]. Here, low-resolution video frames are obtained by down-sampling a Gaussian fuzzy kernel with a standard deviation of \(\sigma=1.6\). The FRVSR method uses explicit motion estimation and compensation to reconstruct video frames. The networks designed using the DUF and PFNL methods transfer motion information in an implicit way. The RBPN method does not use explicit motion compensation, but it calculates the optical flow and inputs it to the network as additional information. The RLSP method adopts a cascaded convolution structure, and the RRN method adopts a cascaded residual block structure. Our method also adopts a cascade structure. The difference is that our method not only cascades residual blocks and regional focus blocks, but it also designs a feature aggregation structure to increase the feature richness of the video frames involved in reconstruction. The quantitative comparison results are shown in Table 3. We calculated PSNR and SSIM in the luminance channel of the YCbCr color space and the RGB color space, respectively. In addition, we also calculated the amount of the model parameters and the average time taken by the network to reconstruct a frame of video.  
It can be seen from Table 3 that the sliding window methods DUF, RBPN, and PFNL are more temporal-consuming than the recurrent structure methods FRVSR, RLSP, and RRN. Therefore, our method adopts a recursive structure, which takes about the same time as other recursive structure methods. Meanwhile, our method has a better performance in PSNR and SSIM than for other methods, which objectively proves the effectiveness of our proposed method.  
Figure 7: Feature visualization of the regional focus block; the green part shows the attention paid to different reconstruction areas of different video frames.  
We select representative 'calendar' and 'city' video sequences from the Vid4 dataset to visually demonstrate our method. As shown in Figure 8, for the 'calendar' video sequence, we intercepted complex texture regions and character regions. Our method can completely restore the texture regions, and the outlines of character regions are also clearer than other methods. For the 'city' video sequence, our method obtains clear building textures and outlines compared with other methods, and the reconstruction results of other methods have blurred or unclear building outlines.  
We select representative 'archpeople' and 'auditorium' video sequences from the Udm10 dataset to visualize the reconstruction results of our method. As shown in Figure 9, for the 'archpeople' video sequence, our method is superior to other methods in reconstructing the details of human hair and the contour details of sunglasses. For the 'auditorium' video sequence, other methods have lost details in the reconstruction of the horizontal axis connection part in the shelf. Our method completely reconstructs this part, and the reconstruction result is similar to the label. The comparison proves that the results of this method are of higher reconstruction quality