Recently, the single image super-resolution (SISR) approaches with deep and
complex convolutional neural network structures have achieved promising
performance. However, those methods improve the performance at the cost of
higher memory consumption, which is difficult to be applied for some mobile
devices with limited storage and computing resources. To solve this problem, we
present a lightweight multi-scale feature interaction network (MSFIN). For
lightweight SISR, MSFIN expands the receptive field and adequately exploits the
informative features of the low-resolution observed images from various scales
and interactive connections. In addition, we design a lightweight recurrent
residual channel attention block (RRCAB) so that the network can benefit from
the channel attention mechanism while being sufficiently lightweight. Extensive
experiments on some benchmarks have confirmed that our proposed MSFIN can
achieve comparable performance against the state-of-the-arts with a more
lightweight model.

本文提出了一种轻量级多尺度特征交互网络（MSFIN）用于图像超分辨率，通过扩展感受野和利用来自各种尺度的低分辨率观察图像的信息特征以及交互连接。此外，设计了一种轻量级的循环残差通道注意块（RRCAB）以便使网络受益于通道注意机制同时保持足够轻量级。与现有技术相比，本文所提出的 MSFIN 能够以更轻量的模型实现可比性能。