标题:Fast image super-resolution with the simplified residual network
作者:Wang C.; Ran L.; He C.
作者机构:[Wang, C] School of Computer Engineering, Jinling Institute of Technology, Nanjing, Jiangsu 211169, China;[ Ran, L] School of Computer Science and Te 更多
通讯作者:Wang, C(wchm87@jit.edu.cn)
通讯作者地址:[Wang, C] School of Computer Engineering, Jinling Institute of TechnologyChina;
来源:Multimedia Tools and Applications
出版年:2020
DOI:10.1007/s11042-020-09954-8
关键词:Convolutional neural networks; Simplified residual network; Super resolution
摘要:Recently, the image super-resolution (SR) methods based on residual learning have obtained remarkable quality performance. However, the current residual-learning methods have low computational performance and slow convergence rate. In this paper, we propose a high-efficiency two-level residual network to make the network learn more useful high-frequency information. Only 5 convolution layers in the LR space are used in our residual network, and no parameters are introduced in the other layers. Compared with the long training time up to several hours or days of previous deep residual networks, our simplified network can make the training time reduce to half an hour. Besides, our simplified network achieves satisfactory quality performance. The evaluation on the public datasets shows that our method can process SR of ultra-high definition (UHD) videos in real-time (more than 24 frames per second) on a generic graphical processing unit (GPU). © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
收录类别:SCOPUS
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091682487&doi=10.1007%2fs11042-020-09954-8&partnerID=40&md5=d4c3cc4005320a4ce79f4cbc1c8277f2
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