论文标题
单图超分辨率方法：调查
Single Image Super-Resolution Methods: A Survey
论文作者
论文摘要
超分辨率（SR）是从一个或多个对同一场景的低分辨率观察中获得高分辨率图像的过程，在过去几十年来，在信号处理和图像处理领域中，一直是一个非常流行的研究主题。由于卷积神经网络的最新发展，随着进入的障碍已大大降低，SR算法的普及飙升。最近，这种受欢迎程度已扩散到视频处理区域，以实时开发SR模型的长度。在本文中，我们比较了专门从事单个图像处理的不同SR模型，并将一目了然地了解它们如何发展以实现多年来的许多不同的目标和形状。
Super-resolution (SR), the process of obtaining high-resolution images from one or more low-resolution observations of the same scene, has been a very popular topic of research in the last few decades in both signal processing and image processing areas. Due to the recent developments in Convolutional Neural Networks, the popularity of SR algorithms has skyrocketed as the barrier of entry has been lowered significantly. Recently, this popularity has spread into video processing areas to the lengths of developing SR models that work in real-time. In this paper, we compare different SR models that specialize in single image processing and will take a glance at how they evolved to take on many different objectives and shapes over the years.
