Acknowledgement
This work was supported by Institute of Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT)(No.2020-0-01826, AI 기반 선도적 실전문제해결 연구인재 양성) This work was supported by the National Research Foundation of Korea(NRF) Grant funded by the Korean Government(MSIP)(No. Grant Number - 2015R1A5A7037615) Following(or This research) was results of a study on the "HPC Support" Project, supported by the 'Ministry of Science and ICT' and NIPA.
References
- C. Dong, C. Loy, K. He, "Image Super-Resolution Using Deep Convolutional Networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.38, No.2 pp.295-307, Feb 2016. https://doi.org/10.1109/TPAMI.2015.2439281
- J. Hu, L. Shen, G. Sun, "Squeeze-and-excitation networks," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Utah, USA, pp.7132-7141, 2018.
- L. Chen, H. Zhang, J. Xiao, L. Nie, J. Shao, W. Liu, T. Chua, "Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, USA, pp.6298-6306, 2017.
- Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, Y. Fu, "Image super-resolution using very deep residual channel attention networks," Proceedings of the European Conference on Computer Vision, Munich, Germany, pp.286-301, 2018.
- T. Dai, J. Cai, Y. Zhang, S. Xia, L. Zhang, "Second-order attention network for single image super-resolution," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, pp. 11065-11074, 2019.
- J. Kim, J. Kwon, K. Lee, "Accurate image super-resolution using very deep convolutional networks," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp.1646-1654, 2016.
- J. Kim, J. Kwon Lee, K. Lee "Deeply-recursive convolutional network for image super-resolution," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp.1637-1645, 2016.
- Y. Tai, J. Yang, X. Liu, "Image super-resolution via deep recursive residual network," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, USA, pp. 3147-3155, 2017.
- B. Lim, S. Son, H. Kim, S. Nah, K. Lee, "Enhanced deep residual networks for single image super-resolution," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, Hawaii, USA, pp.1132-1140, 2017.
- W. Shi, J. Caballero, F. Huszar, J. Totz, A. Aitken, R. Bishop, D. Rueckert, Z. Wang, "Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp.1874-1883, 2016.
- Y. Tai, J. Yang, X. Liu, C. Xu, "Memnet: A persistent memory network for image restoration," Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, pp. 4539-4547, 2017.
- T. Tong, G. Li, X. Liu, Q. Gao, "Image super-resolution using dense skip connections," Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, pp. 4799-4807, 2017.
- Y. Hu, J. Li, Y. Huang, X. Gao, "Channel-wise and spatial feature modulation network for single image super-resolution," IEEE Transactions on Circuits and Systems for Video Technology, Vol.30, No.11, pp.3911-3927, Nov 2020. https://doi.org/10.1109/TCSVT.2019.2915238
- Z. Chen, V. Badrinarayanan, C. Lee, A. Rabinovich, "Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks," Proceedings of the 35th International Conference on Machine Learning, PMLR, Vol.80, pp.794-803, 2018.
- E. Agustsson, R. Timofte, "Ntire 2017 challenge on single image super-resolution: Dataset and study," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, Hawaii, USA, pp.1100-1121, 2017.
- J. Song, H. Cho, J. Yoon, S. Yoon, "Structure adaptive total variation minimization-based image decomposition," IEEE Transactions on Circuits and Systems for Video Technology, Vol.28, No.9, pp.2164-2176, Sep 2018. https://doi.org/10.1109/TCSVT.2017.2717542