Enhanced Back Projection Network Based Stereo Image Super-Resolution Considering Parallax Attention

Abstract

Recent years have witnessed great advances in stereo image super-resolution (SR). However, the existing methods only consider the horizontal parallax when capturing the stereo correspondence, which is insufficient because the vertical parallax inevitably exists in stereo image pairs. To address this problem, we propose an enhanced back projection stereo SR network (EBPSSRnet) to make full use of the complementary information in stereo images for more accurate SR results. Specifically, we propose a relaxed parallax attention module (rePAM) to handle different stereo images with vertical and horizontal parallax. Then, an enhanced back projection block (EBPB) is developed to extract discriminative features for capturing the stereo correspondence and consolidate the best representation for reconstruction. Extensive experiments show that the proposed method achieves state-of-the-art performance on the Flickr1024, Middlebury, KITTI2012 and KITTI2015 datasets.