对比多种CNN超分辨率方法，分析其原理与性能差异。
Application of convolutional neural networks in image super-resolution
- 梳理CNN在图像超分中的主流插值与模块设计
- 实验对比不同方法的重建效果与计算开销
- 适合想了解超分技术演进的开发者与研究者
由于卷积神经网络（CNN）强大的学习能力，其已成为图像超分辨率的主流方法。然而，不同深度学习方法之间存在显著差异，现有文献较少系统总结这些方法之间的关联与区别。为此，本文首先介绍CNN在图像超分辨率中的基本原理，随后详细分析基于双三次插值、最近邻插值、双线性插值、转置卷积、子像素层及元上采样等方法的CNN模型，比较它们在性能上的差异与联系。通过实验评估各方法的重建质量与运行效率，最后指出当前方法的局限性与未来潜在研究方向，为后续研究提供参考。
原文摘要 · Abstract (English)
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.
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