# Cross-View Attention Interaction Fusion Algorithm for Stereo Super-Resolution
## 2 Network Architecture
### Enhanced Cross-View Interaction Strategy  
According to the placement of the attention stereo fusion module, the enhanced cross-view interaction strategy is proposed, as shown in Figure 3, including three parts: vertical sparse connection, horizontal dense connection and feature fusion.  
Vertical sparse fusion. The parameters of the pre-trained single image super-resolution network model are known. For left and right low-resolution images, parameters between two single image super-resolution networks can be shared. Considering that if each level feature in the super-resolution network branch of the previous image and single image are  
Figure 3: Enhanced cross-view interaction.  
input into the attention stereo fusion module, the computational complexity increases and the computational redundancy is increased.  
Therefore, as shown in Figure 3, we set the number of vertical connections to be far less than the number of connections at the network level, and adopted the sparse connection method to input the left and right view features of the upper and lower branches into the attention stereoscopic fusion module at a certain distance to calculate the left and right consistency.  
Transverse dense fusion. In order to make multiple interactions between multiple levels at different depths of the attention stereo fusion module, based on the single image super-resolution network structure of VDSR [8] and SRResNet [26], we propose an adaptive horizontal dense connection method, which acts on the attention stereo fusion module. This method is similar to DenseNet [27] in the feature extraction method, which can perform multiple effective fusion even when the number of attention stereo fusion modules is small. After adding the horizontal connection of the attention stereo fusion module, the parallax attention map of the previous module can be calculated and the input to the next module. The pixel value of the effective mask area can guide the module to learn the next parallax attention map and further strengthen the constraint between stereo consistency.  
In addition, in existing single image super-resolution networks, the number of network layers may not always be the same, resulting in different numbers of stereoscopic attention modules and corresponding changes in the number of horizontal connections. If the number of attention stereo fusion modules is one, there is no need to connect them horizontally; if it is two, the modules are connected to each other. If it is three, the modules are combined in pairs, and so on.  
Feature fusion. The conventional feature fusion operation is used to fuse the features of vertical sparse connections and horizontal dense connections to obtain more discriminative and robust features. The feature fusion operation mainly includes dense layer and transition layer: (1) The dense layer cascades the disparity attention map calculated by each stereo fusion module with the disparity attention map calculated by other stereo fusion modules in the channel dimension. At the same time, the corresponding increase of the number of feature channels leads to the increase of network model parameters. (2) The transition layer controls the number of network model parameters by controlling the number of feature channels. The convolution layer with convolution kernel size of \(1\times 1\) is used to reduce the number of channels, and the average pooled layer with step size of two is used to reduce the height and width by half.