# Cross-View Attention Interaction Fusion Algorithm for Stereo Super-Resolution
## 2 Network Architecture
### Attention Stereo Fusion Module  
In order to integrate the effective information of the original features in the view, we construct an attention stereo fusion module based on parallax attention mechanism. The attention stereo fusion module consists of three blocks: parallax attention block, triple attention block and stereo fusion block. The details are as follows:  
Parallax attention block. Consistent with the parallax attention mechanism first proposed in the literature [16] for stereo super-resolution, the parallax attention map of both perspectives is calculated, and then the effective mask of the left and right images is obtained by comparing the consistency of the parallax between the left and right images. The parallax attention block is used to measure the similarity of the pixel values of two feature images from different perspectives along the horizontal epipolar direction. The details of the parallax attention block are shown in Figure 1.  
Triple attention block. Triple attention block is mainly composed of three parallel branches, as shown in Figure 2. Through rotation operation and residual transformation, the attention weight value of the information in the corresponding view is adjusted, and the information between the two dimensions of channel and space is encoded, so as to establish the dependency relationship between different dimensions. Specifically, given an input feature \(X\in\mathbb{R}^{C\times H\times W}\), it is transferred to the three branches of the triple attention block in parallel. The three branches are the attention computing branch of channel \(C\), the information interaction branch of channel \(C\) and space dimension \(H\), and the information interaction branch of channel \(C\) and space dimension \(W\). Then, the output features of the three branches are averaged and aggregated to generate the final output tensors of size \(C\times H\times W\), which are the left attention image \(F_{\text{att,L}}\) and the right attention image \(F_{\text{att,R}}\) respectively. Taking the attention calculation branch of channel \(C\) dimension as an example, maximum pooling and average pooling operations are used for channel \(C\) dimension, and tensor \(\mathbf{X}_{1}\in\mathbb{R}^{2\times H\times W}\) is obtained by cascading operation. \[\mathbf{X}_{1}=\big{[}H_{\text{max}}(\mathbf{X}),H_{\text{avg}}(\mathbf{X})\big{]} \tag{1}\]  
Then, the tensor obtained by standard convolution with convolution kernel size \(1\times 1\) and batch normalization is used to generate the channel attention weight \(\mathbf{X}_{2}\in\mathbb{R}^{1\times H\times W}\) through the Sigmoid activation function. \[\mathbf{X}_{2}=\sigma(H_{\text{BN}}(H_{3\times 3}(\mathbf{X}_{1}))) \tag{2}\]  
Figure 1: Parallax attention block. Figure 2: Triple attention block. Finally, the output tensor \(\mathbf{X}_{01}\in\mathbb{R}^{C\times H\times W}\) of the first branch is obtained by matrix multiplication between the \(\mathbf{X}_{2}\) calculated by Formula (2) and the input feature \(\mathbf{X}\). \[\mathbf{X}_{01}=\mathbf{X}_{2}\times\mathbf{X} \tag{3}\]  
Stereo fusion block. The stereo fusion block is constructed to connect the similar information provided by the parallax attention mechanism along the horizontal polar direction and the cross-dimensional interactive information obtained by using triple attention. The fusion process is divided into two stages. Take right-to-left conversion as an example, and the specific steps are as follows:  
(1) The first stage: The left and right stereo input features \(\mathbf{F}_{\text{left}}^{\text{in}},\mathbf{F}_{\text{right}}^{\text{in}}\in\mathbb{R }^{H\times W\times C}\) are converted into residual blocks to reduce training conflicts. The transformed features and \(\mathbf{F}_{\text{att\_L}}\) are transferred to the convolution layer with the convolution kernel size of 2 for preliminary fusion to generate \(\mathbf{F}_{0}\in\mathbb{R}^{H\times W\times C}\). \[