# Cross-View Attention Interaction Fusion Algorithm for Stereo Super-Resolution
## 3 Experimental Results
### Quantitative Analysis  
The cross-view attention interaction fusion algorithm is a binocular interaction module built on a single image super-resolution network, which can be applied to various different single image super-resolution branches. The disadvantage is that the algorithm is based on a pre-trained model of monocular super-resolution and relies on its model structure. The more complex the model structure, the more levels the model has. Furthermore, the more attention stereo fusion modules there are, the more horizontal connections there will be. Ultimately, it leads to higher algorithm complexity. Therefore, we focus on analyzing the super-resolution reconstruction accuracy for static stereo images. In order to more accurately evaluate the performance of our algorithm, we used a challenging low-resolution stereo image with a down-sampling factor of four as input, referring to the evaluation method of stereo attention module in reference [19]. On Middlebury, KITTI 2012 and KITTI 2015 datasets, the attention stereo fusion module and enhanced cross-view interaction strategy proposed in this paper are applied to single image super-resolution networks such as SRCNN [7], VDSR [8], SRDenseNet [9], LapSRN [28] and SRResNet [26]. These combined networks are compared with the stereo super-resolution algorithm in literature [19], PASSRnet [16], SPAM [18] and iPASSR [20]. The comparison results are shown in Table 3. In the calculated results, the first row represents the PSNR value and the second row represents the SSIM value. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{1}{c}{**Middlebury**} & **KITTI 2012** & **KITTI 2015** & \multirow{2}{*}{**Average Value**} \\ \cline{2-2} \cline{5-5}  & \(\times\)**4** & \(\times\)**4** & \(\times\)**4** & & \\ \hline \multirow{2}{*}{PASSRnet [16]} & 28.62 & 26.26 & 25.42 & 26.77 \\  & 0.893 & 0.826 & 0.860 & 0.860 \\ \hline \multirow{2}{*}{SPAM [18]} & 29.36 & 26.31 & 24.81 & 26.83 \\  & 0.912 & 0.869 & 0.860 & 0.880 \\ \hline \multirow{2}{*}{iPASSR [20]} & 29.11 & 26.35 & 25.25 & 26.90 \\  & 0.835 & 0.803 & 0.807 & 0.815 \\ \hline \multirow{2}{*}{SRCNN [7] + SAM [19]} & 27.70 & 25.64 & 24.77 & 26.04 \\  & 0.875 & 0.857 & 0.843 & 0.858 \\ \hline \multirow{2}{*}{SRCNN [7] + Ours} & 27.75 & 25.70 & 24.79 & 26.08 \\  & 0.881 & 0.860 & 0.848 & 0.863 \\ \hline \multirow{2}{*}{ VDSR [8] + SAM [19]} & 28.25 & 26.15 & 25.22 & 26.54 \\  & 0.887 & 0.868 & 0.855 & 0.870 \\ \hline \multirow{2}{*}{ VDSR [8] + Ours} & 28.28 & 26.21 & 25.29 & 26.59 \\  & 0.890 & 0.872 & 0.861 & 0.874 \\ \hline \multirow{2}{*}{SRDenseNet [9] + SAM [19]} & 28.14 & 26.10 & 25.17 & 26.47 \\  & 0.885 & 0.866 & 0.853 & 0.868 \\ \hline \multirow{2}{*}{SRDenseNet [9] + Ours} & 28.19 & 26.17 & 25.22 & 26.53 \\  & 0.892 & 0.872 & 0.859 & 0.874 \\ \hline \multirow{2}{*}{ LapSRN [28] + SAM [19]} & 28.25 & 26.15 & 25.20 & 26.53 \\  & 0.888 & 0.868 & 0.855 & 0.870 \\ \hline \multirow{2}{*}{ LapSRN [28] + Ours} & 28.30 & 26.19 & 25.26 & 26.58 \\  & 0.894 & 0.872 & 0.861 & 0.876 \\ \hline \multirow{2}{*}{SRResNet [26] + SAM [19]} & 28.81 & 26.35 & 25.53 & 26.90 \\  & 0.897 & 0.873 & 0.863 & 0.878 \\ \hline \multirow{2}{*}{SRResNet [26] + Ours} & 28.92 & 26.42 & 25.61 & 26.99 \\  & 0.905 & 0.881 & 0.872 & 0.886 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative comparison of stereo super-resolution models. The super-resolution performance performed by our method on Middlebury, KITTI 2012 and KITTI 2015 can be improved accordingly. On the KITTI 2015 dataset, the PSNR and SSIM values calculated by the algorithm based on SRResNet [26] in this paper are 25.61 dB and 0.872, respectively, which has the highest accuracy compared with all algorithms in the Table 3. For example, compared with the highest calculation accuracy based on SRResNet, there is still a PSNR gain of 0.08 dB and a SSIM gain of 0.009.