Most existing approaches for single image super-resolution (SISR) resort to quality low-high resolution (LR-HR) pairs and available degradation kernels to train networks for a specific task in hand in a fully supervised manner. Labeled data used for training are, however, usually limited in terms of the quantity and the diversity degradation kernels. The learned SR networks with one degradation kernel (e.g., bicubic) do not generalize well and their performance sharply deteriorates on other kernels (e.g., blurred or noise). In this paper, we address the critical challenge for SISR: limited labeled LR images and degradation kernels. We propose a novel Semi-supervised Student-Teacher Super-Resolution approach called S${}^2$TSR that super-resolves both labelled and unlabeled LR images via adversarial learning. To better exploit the information from labeled LR images, we propose a student-teacher framework (S-T) via knowledge transfer from supervised learning (T) to unsupervised learning (S). Specifically, the S-T knowledge transfer is based on a shared SR network, partial weight sharing of dual discriminators, and a pair matching network which also plays as a ‘latent discriminator’. Lastly, to learn better features from the limited labeled LR images, we propose a new SR network via non-local and attention mechanisms. Experiments demonstrate that our approach substantially improves unsupervised methods and performs favorably over fully supervised methods.

大多数现有的单图像超分辨率 (SISR) 方法都采用高质量的低高分辨率 (LR-HR) 对和可用的降级内核，以完全监督的方式为手头的特定任务训练网络。然而，用于训练的标记数据通常在数量和多样性退化内核方面受到限制。具有一个退化内核（例如，双三次）的学习 SR 网络不能很好地泛化，并且它们的性能在其他内核（例如，模糊或噪声）上急剧恶化。在本文中，我们解决了 SISR 的关键挑战：有限的标记 LR 图像和退化内核。我们提出了一个新颖的小号EMI监督小号tudent-牛逼eacher小号uper- ř esolution方法称为小号${}^2$TSR通过对抗性学习超解析标记和未标记的 LR 图像。为了更好地利用标记 LR 图像中的信息，我们通过从监督学习 (T) 到无监督学习 (S) 的知识转移，提出了一个学生-教师框架 (ST)。具体来说，ST 知识转移基于共享的 SR 网络、双鉴别器的部分权重共享以及同时充当“潜在鉴别器”的配对匹配网络。最后，为了从有限的标记 LR 图像中学习更好的特征，我们通过非局部和注意机制提出了一个新的 SR 网络。实验表明，我们的方法大大改进了无监督方法，并且优于完全监督方法。