Recently, the methods based on implicit neural representations have shown
excellent capabilities for arbitrary-scale super-resolution (ASSR). Although
these methods represent the features of an image by generating latent codes,
these latent codes are difficult to adapt for different magnification factors
of super-resolution, which seriously affects their performance. Addressing
this, we design Multi-Scale Implicit Transformer (MSIT), consisting of an
Multi-scale Neural Operator (MSNO) and Multi-Scale Self-Attention (MSSA). Among
them, MSNO obtains multi-scale latent codes through feature enhancement,
multi-scale characteristics extraction, and multi-scale characteristics
merging. MSSA further enhances the multi-scale characteristics of latent codes,
resulting in better performance. Furthermore, to improve the performance of
network, we propose the Re-Interaction Module (RIM) combined with the
cumulative training strategy to improve the diversity of learned information
for the network. We have systematically introduced multi-scale characteristics
for the first time in ASSR, extensive experiments are performed to validate the
effectiveness of MSIT, and our method achieves state-of-the-art performance in
arbitrary super-resolution tasks.

最近，基于隐式神经表示的方法在任意尺度超分辨率（ASSR）方面展现出了出色的能力。我们设计了多尺度隐式变换器（MSIT），其中包含一个多尺度神经操作器（MSNO）和多尺度自注意力（MSSA）。通过功能增强、多尺度特征提取和多尺度特征融合，MSNO 获得了多尺度潜在编码。MSSA 进一步增强了潜在编码的多尺度特征，从而提高了性能。此外，为了改进网络性能，我们提出了与累积训练策略相结合的重互作用模块（RIM），以改进网络学习信息的多样性。我们首次在 ASSR 中系统地引入了多尺度特征，并进行了大量实验证明了 MSIT 的有效性，我们的方法在任意超分辨率任务中取得了最先进的性能。