  Stereo image super-resolution aims at enhancing the quality of
super-resolution results by utilizing the complementary information provided by
binocular systems. To obtain reasonable performance, most methods focus on
finely designing modules, loss functions, and etc. to exploit information from
another viewpoint. This has the side effect of increasing system complexity,
making it difficult for researchers to evaluate new ideas and compare methods.
This paper inherits a strong and simple image restoration model, NAFNet, for
single-view feature extraction and extends it by adding cross attention modules
to fuse features between views to adapt to binocular scenarios. The proposed
baseline for stereo image super-resolution is noted as NAFSSR. Furthermore,
training/testing strategies are proposed to fully exploit the performance of
NAFSSR. Extensive experiments demonstrate the effectiveness of our method. In
particular, NAFSSR outperforms the state-of-the-art methods on the KITTI 2012,
KITTI 2015, Middlebury, and Flickr1024 datasets. With NAFSSR, we won 1st place
in the NTIRE 2022 Stereo Image Super-resolution Challenge. Codes and models
will be released at https://github.com/megvii-research/NAFNet.
