Non-Local Nested Residual Attention Network for Stereo Image Super-Resolution

Nowadays CNN-based stereo image super-resolution(SR) methods have obtained remarkable performance. However, most of existing methods only superficially portrayed the low layer features without considering the uneven distribution of information, which is insufficient because stereo image warping and sub-pixel upsampling require discriminative features to identify corresponding pixels. To address this problem, in this paper, we propose a novel network named Non-local Nested Residual Attention Network (NNRANet). Specifically, a non-local dilated attention module (NDAM) is developed to exploit the rich hierarchical feature and capture the long-range dependencies between pixels. Moreover, we present a nested residual group (NRG) with dense connections and multiple nested residual sub-network, which not only continuously remembers and extracts the stereo fusion feature, but also enables training a deeper and more stable network. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance on the Middlebury, KITTI 2012 and KITTI 2015 datasets.