Capturing Cross-Scale Disparity for Stereo Image Super-Resolution

Stereo image super-resolution (SR) exploits the stereo feature information from cross-view image pairs for image resolution. This paper focuses on how to effectively exploit the disparity information between stereo viewpoints and proposes a cross-scale parallax-attention network (CSPAN) for stereo image SR. Specifically, a novel cross-scale parallax attention module (CPAM) is developed to explore cross-scale parallax prior. Moreover, instead of the widely used upsampling module based on the sub-pixel layer, we present a novel cascade dynamic upsampling module (CDUM), which not only dynamically generates the upsampling filters according to the input content, but also restores the high frequency details in a coarse-to-fine manner. Especially, nonlinear activation free blocks (NAFBlocks) are used as the feature extraction module in our network, which further improves the performance of our model. Extensive experiments on Middlebury, KITTI 2012 and KITTI 2015 demonstrate that the proposed framework outperforms many competitive stereo SR methods in both PSNR and SSIM. Code is available at https://github.com/DoragonKuesuto/CSPAN.