  A light-weight super-resolution (LSR) method from a single image targeting
mobile applications is proposed in this work. LSR predicts the residual image
between the interpolated low-resolution (ILR) and high-resolution (HR) images
using a self-supervised framework. To lower the computational complexity, LSR
does not adopt the end-to-end optimization deep networks. It consists of three
modules: 1) generation of a pool of rich and diversified representations in the
neighborhood of a target pixel via unsupervised learning, 2) selecting a subset
from the representation pool that is most relevant to the underlying
super-resolution task automatically via supervised learning, 3) predicting the
residual of the target pixel via regression. LSR has low computational
complexity and reasonable model size so that it can be implemented on
mobile/edge platforms conveniently. Besides, it offers better visual quality
than classical exemplar-based methods in terms of PSNR/SSIM measures.
