标题： 卷积神经网络在图像超分辨率中的应用 显示英文标题

标题： Application of convolutional neural networks in image super-resolution

摘要： 由于卷积神经网络（CNNs）具有强大的学习能力，它们已成为图像超分辨率的主流方法。然而，不同类型的深度学习方法之间存在很大差异。关于图像超分辨率中不同方法的关系和差异的文献很少。因此，根据设备的加载能力和执行速度来总结这些文献是很重要的。 本文首先介绍了CNNs在图像超分辨率中的原理，然后介绍了基于CNNs的双三次插值、最近邻插值、双线性插值、转置卷积、亚像素层、元上采样等方法用于图像超分辨率，分析了不同基于CNNs的插值和模块之间的差异和关系，并通过实验比较了这些方法的性能。最后，本文给出了潜在的研究方向和缺点，并对全文进行了总结，这有助于推动CNNs在图像超分辨率领域的发展。 显示英文摘要

摘要： Due to strong learning abilities of convolutional neural networks (CNNs), they have become mainstream methods for image super-resolution. However, there are big differences of different deep learning methods with different types. There is little literature to summarize relations and differences of different methods in image super-resolution. Thus, summarizing these literatures are important, according to loading capacity and execution speed of devices. This paper first introduces principles of CNNs in image super-resolution, then introduces CNNs based bicubic interpolation, nearest neighbor interpolation, bilinear interpolation, transposed convolution, sub-pixel layer, meta up-sampling for image super-resolution to analyze differences and relations of different CNNs based interpolations and modules, and compare performance of these methods by experiments. Finally, this paper gives potential research points and drawbacks and summarizes the whole paper, which can facilitate developments of CNNs in image super-resolution.