Parallax-Based Spatial and Channel Attention for Stereo Image Super-Resolution

Stereo images can provide more extra information to enhance performance of super-resolution (SR) owing to two different visual angles. Due to the attention mechanism has been widely employed in various computer vision tasks and achieved excellent performance, the recent methods for stereo SR utilize attention mechanism to obtain stereo correspondence. However, their attention mechanisms are generally spatial since they treat channel-wise features equally, which result in ignoring a lot of valuable information contained in the channels. In this paper, we propose a parallax-based spatial and channel attention stereo SR network (PSCASSRnet) to combine the stereo correspondence with the SR task. Specifically, we present a parallax-based spatial and channel attention module (PSCAM) to take full advantage of spatial and channel-wise characteristics to obtain the stereo correspondence. The extensive experiments prove that our PSCAM can produce more appropriate attention map with all possible disparities between the stereo image pair to achieve better SR performance, and our PSCASSRnet outperforms other state-of-the-art methods in the aspects of visual quality and quantitive on the three mainstream public datasets named KITTI2012, KITTI2015 and Middlebury respectively.