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 with small samples. However, there are little literatures summarizing different GANs in SISR. In this paper, we conduct a comparative study of GANs from different perspectives. We first take a look at developments of GANs. Second, we present popular architectures for GANs in big and small samples for image applications. Then, we 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. Next, we compare performance 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（SISR）在图像处理领域扮演着重要角色。近年来，生成对抗网络（GANs）能够在少量样本的低分辨率图像上取得优异效果。然而，目前鲜有文献系统总结SISR中不同的GAN方法。本文从多个角度对GANs进行了比较研究。首先回顾GANs的发展历程；其次，分别介绍大样本与小样本场景下图像应用中流行的GAN架构。接着，从监督、半监督和无监督三个层面，分析基于优化方法和判别学习的GANs在图像超分辨率中的设计动机、实现方式与差异，这些GANs通过整合不同网络架构、先验知识、损失函数及多任务策略进行解析。随后，通过定量与定性分析，比较这些主流GANs在公开数据集上的SISR性能表现。最后，指出GANs面临的挑战及SISR领域的潜在研究方向。