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.
翻译:使用望远镜系统提供的补充信息,提高超分辨率图像的超分辨率,目的是提高超分辨率结果的质量。为了取得合理的性能,大多数方法侧重于精细设计模块、损失功能等,以便从另一个角度利用信息。这具有系统复杂性不断提高的副作用,使研究人员难以评估新的想法和比较方法。本文继承了一个强大而简单的图像恢复模型,即NAFNet,用于单视特征提取,并通过在各种观点之间添加对引信特性的交叉关注模块以适应望远镜情景来扩展该模型。拟议的立体图像超分辨率基线作为NAFSSR。此外,还拟议培训/测试战略以充分利用NAFSSR的性能。广泛的实验展示了我们的方法的有效性。特别是,NAFSSR在2012年KITTI、KITTI、2015年Midbury和Flick1024数据集上超越了最新的方法。与NAFSSR一起,我们赢得了NTIRE 2022 立体图像超分辨率挑战的首个位置。我们将在https://giressreub/FVII上公布代码和模型。