Single image super-resolution (SISR) has played an important role in the field of image processing. Recent generative adversarial networks (GANs) can achieve excellent results on low-resolution images. However, there are little literatures summarizing different GANs in SISR. In this paper, we conduct a comparative study of GANs from different perspectives. We begin by surveying the development of GANs and popular GAN variants for image-related applications, and then analyze motivations, implementations and differences of GANs based optimization methods and discriminative learning for image super-resolution in terms of supervised, semi-supervised and unsupervised manners, where these GANs are analyzed via integrating different network architectures, prior knowledge, loss functions and multiple tasks. Secondly, we compare the performances of these popular GANs on public datasets via quantitative and qualitative analysis in SISR. Finally, we highlight challenges of GANs and potential research points for SISR.

翻译：单图像超分辨率在图像处理领域具有重要作用。近年来，生成对抗网络在低分辨率图像上取得了优异的效果。然而，目前鲜有文献系统总结单图像超分辨率中不同的生成对抗网络方法。本文从多角度对生成对抗网络进行了比较研究。首先，我们综述了生成对抗网络的发展历程及其在图像相关任务中的主流变体，随后从监督、半监督和无监督三个层面，通过整合不同网络架构、先验知识、损失函数与多任务学习，系统分析了基于优化方法和判别学习的生成对抗网络在图像超分辨率中的设计动机、实现方式与差异特性。其次，我们通过定量与定性分析，对比了这些主流生成对抗网络在公开数据集上的单图像超分辨率性能。最后，我们指出了生成对抗网络在单图像超分辨率中面临的挑战及潜在研究方向。