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.

翻译：本文提出了一种面向移动应用的轻量级单图像超分辨率（LSR）方法。LSR采用自监督框架预测插值后的低分辨率（ILR）图像与高分辨率（HR）图像之间的残差图像。为降低计算复杂度，LSR未采用端到端优化的深度网络。该方法由三个模块组成：1）通过无监督学习在目标像素邻域内生成丰富多样化的表征池；2）通过监督学习从表征池中自动选取与底层超分辨率任务最相关的子集；3）通过回归方法预测目标像素的残差。LSR具有较低的计算复杂度和合理的模型尺寸，可便捷部署于移动/边缘平台。此外，在PSNR/SSIM指标上，其视觉质量优于传统基于示例的方法。