Recently, the performance of single image super-resolution (SR) has been
significantly improved with powerful networks. However, these networks are
developed for image SR with a single specific integer scale (e.g., x2;x3,x4),
and cannot be used for non-integer and asymmetric SR. In this paper, we propose
to learn a scale-arbitrary image SR network from scale-specific networks.
Specifically, we propose a plug-in module for existing SR networks to perform
scale-arbitrary SR, which consists of multiple scale-aware feature adaption
blocks and a scale-aware upsampling layer. Moreover, we introduce a scale-aware
knowledge transfer paradigm to transfer knowledge from scale-specific networks
to the scale-arbitrary network. Our plug-in module can be easily adapted to
existing networks to achieve scale-arbitrary SR. These networks plugged with
our module can achieve promising results for non-integer and asymmetric SR
while maintaining state-of-the-art performance for SR with integer scale
factors. Besides, the additional computational and memory cost of our module is
very small.

本文提出了一种基于不同比例尺下特征适应块和上采样层的单幅图像超分辨率网络的方法，并利用特定比例尺下的网络知识，提出了一种比例尺 - aware 知识转移方法。结果表明，该方法可以在保持整数比例尺下的图像超分辨率性能的同时，在非整数和非对称跨度方面取得有前途的结果，而附加的计算和内存成本非常小。