  Although numerous solutions have been proposed for image super-resolution,
they are usually incompatible with low-power devices with many computational
and memory constraints. In this paper, we address this problem by proposing a
simple yet effective deep network to solve image super-resolution efficiently.
In detail, we develop a spatially-adaptive feature modulation (SAFM) mechanism
upon a vision transformer (ViT)-like block. Within it, we first apply the SAFM
block over input features to dynamically select representative feature
representations. As the SAFM block processes the input features from a
long-range perspective, we further introduce a convolutional channel mixer
(CCM) to simultaneously extract local contextual information and perform
channel mixing. Extensive experimental results show that the proposed method is
$3\times$ smaller than state-of-the-art efficient SR methods, e.g., IMDN, in
terms of the network parameters and requires less computational cost while
achieving comparable performance. The code is available at
https://github.com/sunny2109/SAFMN.
