  Deep learning has been successfully applied to the single-image
super-resolution (SISR) task with great performance in recent years. However,
most convolutional neural network based SR models require heavy computation,
which limit their real-world applications. In this work, a lightweight SR
network, named Adaptive Weighted Super-Resolution Network (AWSRN), is proposed
for SISR to address this issue. A novel local fusion block (LFB) is designed in
AWSRN for efficient residual learning, which consists of stacked adaptive
weighted residual units (AWRU) and a local residual fusion unit (LRFU).
Moreover, an adaptive weighted multi-scale (AWMS) module is proposed to make
full use of features in reconstruction layer. AWMS consists of several
different scale convolutions, and the redundancy scale branch can be removed
according to the contribution of adaptive weights in AWMS for lightweight
network. The experimental results on the commonly used datasets show that the
proposed lightweight AWSRN achieves superior performance on x2, x3, x4, and x8
scale factors to state-of-the-art methods with similar parameters and
computational overhead. Code is avaliable at:
https://github.com/ChaofWang/AWSRN
