# Cross-View Attention Interaction Fusion Algorithm for Stereo Super-Resolution
[18] proposed an algorithm combining the two mechanisms of self-attention and parallax attention, and aggregated the features in the image view and the information features of the stereo image at the same time, so as to reconstruct the stereo super-resolution image pair with high quality. In addition, The Stereo Attention Module [19] is proposed in view of the excellent performance of the existing single image super-resolution methods in utilizing the information in the view. By using the prior knowledge of the single image super-resolution network, multiple three-dimensional attention modules are inserted between the branches of the single image super-resolution network of the left and right images, so as to realize multiple interactions between the two view information and improve the performance of the network reconstruction of image details. [PERSON] et al. [20] designed a twin network based on bidirectional parallax attention module by using symmetric cues in stereo image pairs. The two views are computed in a highly symmetrical manner to improve the performance of stereo super-resolution. However, the pixel offset between stereo image pairs and the sub-pixel up-sampling used in the reconstruction process require discriminant features to identify the corresponding pixels. Since the parallax between left and right images is variable, [PERSON] et al. [21] proposed a parallax feature alignment module to use parallax information for feature alignment and fusion. [PERSON] et al. [22] proposed a cross-view capture network to achieve super-resolution of stereoscopic images by designing cross-view blocks and cascaded spatial awareness modules. [PERSON] et al. [23] proposed a universal parallax attention mechanism to capture stereo correspondence by calculating the feature similarity along the epipolar line, without considering parallax changes. [PERSON] et al. [24] proposed to take the swin transformer as the framework and combine the bidirectional parallax attention module to maximize the auxiliary information provided by the binocular structure. [PERSON] et al. [25] proposed NAFNet, a powerful and simple image recovery model, for single-view feature extraction and fused features between views by adding cross-attention modules to suit binocular scenes. Although some achievements have been made in the research of convolutional neural networks in super-resolution learning, the following problems still exist: (1) The prior knowledge learned from the single image super-resolution algorithm is not fully utilized in the binocular image super-resolution task; (2) There is a lack of new mechanisms for efficient use of information from the left and right views, and information within and between views is not communicated adequately. Therefore, we use parallax attention to calculate the pixel similarity of two images along the epipolar axis, and use the triple attention mechanism to integrate the cross-dimensional information of the corresponding view, so as to build a stereo fusion block. Then, we propose an enhanced cross-view interaction strategy to make the stereo fusion block work cooperatively between two single image super-resolution networks to integrate cross-view information and in-view information.