  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.
