Open Access
Issue
ITM Web Conf.
Volume 32, 2020
International Conference on Automation, Computing and Communication 2020 (ICACC-2020)
Article Number 03044
Number of page(s) 6
Section Computing
DOI https://doi.org/10.1051/itmconf/20203203044
Published online 29 July 2020
  1. S. Schulter, C. Leistner and H. Bischof, “ Fast and accurate image upscaling with superresolution forests “, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015, pp. 3791-3799. doi: 10.1109/CVPR.2015.7299003. [Google Scholar]
  2. C. Dong, C.C. Loy, K. He, and X. Tang, “Learning a deep convolutional network for image super-resolution “, in Computer Vision–ECCV 2014, pp. 184-199. Springer, 2014. [Google Scholar]
  3. C. Dong, C.C. Loy, K. He, and X. Tang, “Image superresolution using deep convolutional networks “, IEEE transactions on pattern analysis and machine intelligence, Vol. 38, no. 2, pp. 295-307, 2016. [Google Scholar]
  4. W. Yang, X. Zhang, Y. Tian, W. Wang, J. Xue and Q. Liao, “ Deep Learning for Single Image Super-Resolution: A Brief Review”,in IEEE Transactions on Multimedia. doi:10.1109/TMM.2019.2919431. [Google Scholar]
  5. Dong, Chao, C.C. Loy, and Xiaoou Tang. “Accelerating the super-resolution convolutional neural network”. European Conference on Computer Vision. 2016. [Google Scholar]
  6. David C. Van Essen, Stephen M. Smith, Deanna M. Barch, Timothy E.J. Behrens, Essa Yacoub, Kamil Ugurbil, for the WUMinn HCP Consortium. (2013). The WU-Minn Human Connectome Project: An overview. NeuroImage 80(2013):62-79. [Google Scholar]
  7. CT-image Super Resolution Using 3D Convolutional Neural Network by Yukai Wang, Qizhi Teng, Xiao-hai He, Junxi Feng, Tingrong Zhang (Jun 2018). [Google Scholar]
  8. Yuhua Chen, Feng Shi, Anthony G. Christodoulou, Zhengwei Zhou, Yibin Xie, Debiao Li, “Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network”,(Jun 2018). [Google Scholar]
  9. Chao Dong, Chen Change Loy, Xiaoou Tang, “Accelerating the Super-Resolution Convolutional Neural Network”,(Aug 2016). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.