论文标题
图像超分辨率的生成对抗网络：调查
Generative Adversarial Networks for Image Super-Resolution: A Survey
论文作者
论文摘要
单图像超分辨率（SISR）在图像处理领域起着重要作用。最近的生成对抗网络（GAN）可以在带有小样本的低分辨率图像上取得出色的效果。但是，很少有文献总结了SISR中不同的剂量。在本文中，我们从不同角度进行了对甘种的比较研究。我们首先看一下甘斯的发展。其次，我们为图像应用中的大型和小样本中提供了流行的gan架构。然后，我们分析了基于gan的优化方法的动机，实施和差异，以及图像超分辨率的歧视性学习，以受监督，半监督和无监督的方式来分析，在其中通过集成不同的网络体系结构，先验知识，损失功能和多个任务来分析这些gans。接下来，我们通过SISR中的定量和定性分析在公共数据集上比较了这些受欢迎的gan的性能。最后，我们重点介绍了gan的挑战和SISR的潜在研究点。
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.
