# Cross-View Attention Interaction Fusion Algorithm for Stereo Super-Resolution
## 3 Experimental Results
### Ablation Analysis
#### 3.2.2 Ablation Analysis of Enhanced Cross-View Interaction Strategy  
Based on ablation experiment of the attention stereo fusion module, ablation experiments are carried out for the combination mode between the attention stereo fusion module and the super-resolution branch of the previous single image as well as the interaction mode between the attention stereo fusion modules and mainly includes three parts: vertical connection, horizontal connection and feature fusion. The ablation experiments of enhanced cross-view interaction strategy are shown in Table 2 where VSC, VDC, TSC and FS represent vertical sparse connection, vertical dense connection, transverse sparse connection and feature summation, respectively. The symbol indicates that the corresponding block is selected.  
Although it can be concluded from the ablation experiments of attention stereo fusion modules that, within a certain range, the performance improves to a small degree with the increase in the number of attention stereo fusion modules. It can be seen from Table 2 that the attention stereo fusion module is inserted between the super-resolution branches of a single image through vertical dense connection, which does not significantly improve the performance compared with sparse connection but adds many connections. In the process of network learning, there is overlap and redundancy of information between adjacent levels. Therefore, it can be proved that the proposed vertical sparse connection is beneficial to enhance the information interaction between views.  
In addition, it can be seen from Table 2 that the horizontal dense connection between attention stereo fusion modules has significantly improved performance on both datasets compared with sparse connection. It can be concluded that the horizontal dense connection between attention stereo fusion modules increases the multi-level connection of information between views, which improves the performance of stereo super-resolution task in reconstructing details. As the number of modules increases further, the performance will eventually become saturated. In the single image super-resolution network, the feature difference between adjacent levels is not obvious, and the view information has been fully utilized, further increasing the number of modules can only provide a small improvement. The feature fusion method also plays a very important role in the network with many connections. The improved accuracy is higher than that of vertical sparse connection and horizontal dense connection. It is proved that the proposed feature fusion method is effective and can further improve the network performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multicolumn{8}{c}{**Connection and Fusion Settings**} & \multicolumn{4}{c}{**Middlebury**} & \multicolumn{2}{c}{**KITTI 2015**} \\ \hline
**VSC** & **VDC** & **TSC** & **TDC** & **FS** & **Ours** & \
\begin{tabular}{c} **PSNR** \\ **(dB)** \\ \end{tabular} & **SSIM** &
\begin{tabular}{c} **PSNR** \\ **(dB)** \\ \end{tabular} & **SSIM** \\ \hline \(\boldsymbol{\check{\
u}}\) & & & & & & 28.21 & 0.880 & 25.23 & 0.851 \\ \(\boldsymbol{\check{\
u}}\) & & & & & 28.23 & 0.883 & 25.26 & 0.854 \\ \(\boldsymbol{\check{\
u}}\) & & & & & \(\boldsymbol{\check{\
u}}\) & 28.24 & 0.883 & 25.24 & 0.855 \\ \hline \end{tabular}
\end{table}
Table 2: Ablation experiment on enhanced cross-view interaction strategy.