ITM Web Conf.
Volume 32, 2020International Conference on Automation, Computing and Communication 2020 (ICACC-2020)
|Number of page(s)||6|
|Published online||29 July 2020|
- 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. [CrossRef] [Google Scholar]
- 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]
- 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. [CrossRef] [Google Scholar]
- 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]
- Dong, Chao, C.C. Loy, and Xiaoou Tang. “Accelerating the super-resolution convolutional neural network”. European Conference on Computer Vision. 2016. [Google Scholar]
- 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]
- 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]
- 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]
- Chao Dong, Chen Change Loy, Xiaoou Tang, “Accelerating the Super-Resolution Convolutional Neural Network”,(Aug 2016). [Google Scholar]
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