# Cross-View Attention Interaction Fusion Algorithm for Stereo Super-Resolution
## 3 Experimental Results
### Qualitative Analysis  
Outdoor detailed reconstruction is more challenging than indoor reconstruction, so we select multiple datasets for different outdoor scenes, and select distant and close range objects with different definitions to verify the generalization ability of the model. In addition, since low-quality images with a sampling factor of four are more difficult to reconstruct than images with a sampling factor of two, qualitative experiments are conducted using low-quality images with a sampling factor of four for comparison. On the Flickr 1024 dataset, the proposed algorithm is applied to monocular image super-resolution networks such as SRCNN [7], SRDenseNet [9], VDSR [8], LapSRN [28] and SRResNet [26], and the qualitative results of these networks and PASSRnet [16] are analyzed. The visual comparison with sampling factor of four is shown in Figure 4. The two positions of a graph in the data set are enlarged, and the result graphs of different algorithms are displayed at the same magnification. As can be seen from Figure 4, the effect of detail restoration can be analyzed more intuitively by comparing the original high-resolution image. For example, in the super-resolution result map calculated by combining SRResNet and our algorithm, the contours around the letters V, I and A are more complete, and the details are richer and clearer; in the eaves position, compared with the results of other algorithms, the details of stripes and holes recovered by our algorithm are more complete and clearer. It can be proved that our attention stereo fusion algorithm can obtain better detail restoration effect, and the enhanced cross-view interaction strategy is helpful to further improve the reconstruction performance of stereo super-resolution network. As the accuracy of stereo super-resolution reconstruction depends on the similarity between the corresponding pixels in the horizontal polar direction of the left and right views, the stereo consistency between the left and right views is reflected in the effect of image detail restoration. Experimental results show that our method can well maintain the stereo consistency of corresponding pixels between stereo views. In addition to testing remote image, the close range image in KITTI 2012 dataset [30] is also tested. The visual comparison of the results with sampling factor of four is shown in Figure 5. It can be seen that our algorithm has more obvious edges in the reconstruction results at the window eaves of the house; at the fence position, the resulting image is also clearer and more orderly than other algorithms, greatly reducing blur and ghost. For these two amplified positions, the PSNR and SSIM values calculated by this algorithm are higher than those of other algorithms. Therefore, the attention stereo fusion module mentioned in this method can enhance the ability of feature recognition and network learning and reasoning, thereby maintaining good stereoscopic consistency of corresponding pixels between stereoscopic views. In addition, the enhanced cross-view interaction strategy also greatly improves the information exchange between different branches and different modules, and the information transmission ability is stronger. In addition, consistent with the comparison algorithm adopted on the KITTI 2012 dataset, the qualitative results with a sampling factor of four were also compared and analyzed on the KITTI 2015 dataset [31]. As shown in Figure 6, super-resolution visual comparison of the two positions of the intake grille and road sign is observed, including the original high-resolution image, the reconstruction results of our algorithm and other algorithms. The results are displayed at the same magnification for clearer observation and analysis. As can be seen from Figure 6, in the result graph recovered by SRCNN, LapSRN [28] and SRResNet [26] combined with our method, the details and contours of the arrow and disc are clearer and more complete, and the two stripes at the position of the air inlet of the automobile engine are smoother without crossing.