Super resolution problems are widely discussed in medical imaging. Spatial resolution of medical images are not sufficient due to the constraints such as image acquisition time, low irradiation dose or hardware limits. To address these problems, different super resolution methods have been proposed, such as optimization or learning-based approaches. Recently, deep learning methods become a thriving technology and are developing at an exponential speed. We think it is necessary to write a review to present the current situation of deep learning in medical imaging super resolution. In this paper, we first briefly introduce deep learning methods, then present a number of important deep learning approaches to solve super resolution problems, different architectures as well as up-sampling operations will be introduced. Afterwards, we focus on the applications of deep learning methods in medical imaging super resolution problems, the challenges to overcome will be presented as well.

超分辨率问题在医学成像中得到了广泛讨论。由于诸如图像获取时间，低辐射剂量或硬件限制之类的限制，医学图像的空间分辨率不足。为了解决这些问题，已经提出了不同的超分辨率方法，例如优化或基于学习的方法。近年来，深度学习方法已成为一种蓬勃发展的技术，并且正以指数级的速度发展。我们认为有必要写一篇综述来介绍医学成像超分辨率中深度学习的现状。在本文中，我们首先简要介绍了深度学习方法，然后介绍了解决超分辨率问题的许多重要深度学习方法，将介绍不同的体系结构以及上采样操作。然后，