Learning continuous image representations is recently gaining popularity for
image super-resolution (SR) because of its ability to reconstruct
high-resolution images with arbitrary scales from low-resolution inputs.
Existing methods mostly ensemble nearby features to predict the new pixel at
any queried coordinate in the SR image. Such a local ensemble suffers from some
limitations: i) it has no learnable parameters and it neglects the similarity
of the visual features; ii) it has a limited receptive field and cannot
ensemble relevant features in a large field which are important in an image. To
address these issues, this paper proposes a continuous implicit
attention-in-attention network, called CiaoSR. We explicitly design an implicit
attention network to learn the ensemble weights for the nearby local features.
Furthermore, we embed a scale-aware attention in this implicit attention
network to exploit additional non-local information. Extensive experiments on
benchmark datasets demonstrate CiaoSR significantly outperforms the existing
single image SR methods with the same backbone. In addition, CiaoSR also
achieves the state-of-the-art performance on the arbitrary-scale SR task. The
effectiveness of the method is also demonstrated on the real-world SR setting.
More importantly, CiaoSR can be flexibly integrated into any backbone to
improve the SR performance.

本文提出了一种连续的隐式关注网络，称为 CiaoSR，以解决在单张图片超分辨率重建中可能遇到的局部模型信息不足、感受野受限的问题，并在多个基准数据集上实验表明 CiaoSR 显著优于现有的单个图像超分辨率方法，并取得了任意比例超分辨率任务的最佳性能。