# Video Super-Resolution Network with Gated High-Low Resolution Frames
## 4 Experimental Results
### Ablation Study  
* Ablation Experiment I  
To verify the effectiveness of the modules proposed in this paper, we trained three different models as pre-initial hidden state network (IHSNet), gated adjacent high- and low-resolution frame alignment network (GAFANet), and scale local hierarchical significant feature fusion network (SLLFNet) for validation. The different models were trained uniformly on Vimeo-90K-T, containing 7824 sequential frames, and their average PSNR (dB) and SSIM performance results on the dataset from the VSR test set Vid4 are shown in Table 1. It can be observed that the lack of any of the modules results in different degradations in performance, indicating that each module is necessary. The specific reasons are as follows: (1) The pre-initial hidden state network (IHSNet) during the one-way loop and the initial hidden state information can reduce the effect of unbalanced output video frames. At the same time, the initial hidden state information can also improve the super-resolution reconstruction effect. (2) The gated adjacent high-low resolution frame alignment network (GAFANet) can use the high-low resolution neighboring frame information simultaneously, effectively improving the accuracy of the motion compensation and reconstruction effect. The introduction of the gating mechanism, on the other hand, enables the adaptive selection of useful information to avoid the effect of motion estimation errors. (3) The scale local level significant feature fusion network (SLLFNet) is further designed to enhance the reconstruction quality of video by fusing the features used \begin{table}
\begin{tabular}{c c c c} \hline \hline
**IHSNet** & **GAFANet** & **SLLFNet** & **PSNR/SSIM** \\ \hline  & \(\surd\) & \(\surd\) & 27.42/0.8381 \\ \(\surd\) & & \(\surd\) & 27.25/0.8336 \\ \(\surd\) & \(\surd\) & & 26.25/0.7886 \\ \(\surd\) & \(\surd\) & \(\surd\) & 27.47/0.8372 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of ablation experiment I at 4\(\times\) on Vid4. Figure 8: (**a**) Plot of network training loss; (**b**) plot of test performance evaluation. to fuse the aligned video frames and extracting high-frequency detailed information of the edges and textures of the video frames. As a result, the average performance improvement is 0.05 (dB)/0.0001 with IHSNet; 0.22 (dB)/0.046 with GAFANet; and (3) 1.22 (dB)/0.0504 with SLLFNet. * Ablation Experiment II  
To verify the effectiveness of the gating-based motion compensation, we trained two different network models. One is a model with gated motion compensation and the other was without gated motion compensation, denoted by GHLRF-w and GHLRF-o, with and without, respectively. The average PSNR (dB) and SSIM performance results on the dataset from the VSR test set Vid4, trained uniformly on Vimeo-90K-T containing 7824 sequence frames, are shown in Table 2. It can be seen that the reconstruction performance of the motion-compensated GHLRF-w with gating is better than that of the motion-compensated GHLRF-o without gating, with the average performance increasing from 27.28 (dB)/0.83460 to 27.47 (dB)/0.8346; it can thus be seen that, on alignment, the use of the gating mechanism adaptively selects useful information in high-low resolution neighboring frames to avoid the effect of estimation errors. * Ablation Experiment III  
To further verify that the simultaneous use of high-low resolution information LHRA is valid in comparison to the other two methods, we compared them with aligned LRA using only low-resolution frames, aligned HRA using only high-resolution frames, and with high-resolution HR optical flow operations. We trained three different models as LHRA, LRA, and HRA. The different models were trained uniformly on Vimeo-90K-T, containing 7824 sequence frames, and their average PSNR (dB) and SSIM performance results on the dataset of the VSR test set Vid4 are shown in Table 3. The LHRA method achieves the best PSNR, and the SSIM is in between HRA and LRA.