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：在这项工作中,提出了针对移动应用的单一图像的轻量超分辨率(LSR)方法。LSR预测了内插低分辨率(ILR)和高分辨率(HR)图像之间的剩余图像,使用自监管的框架。为降低计算复杂性,LSR没有采用端到端优化深度网络。它由三个模块组成:1)通过不受监督的学习,在目标像素附近建立一个丰富和多样化的代表库;2)从代表库中选择一个通过监管的学习自动与基本超级分辨率任务最相关的子集;3)通过回归预测目标像素的剩余。LSR的计算复杂性较低,并且具有合理的模型大小,因此可以方便地在移动/高级平台上实施。此外,在PSNR/SSIM措施方面,它提供了比经典的示范方法更好的视觉质量。</s>