Acknowledgement
This work was supported by the Technology development Program(S2840023) funded by the Ministry of SMEs and Startups(MSS, Korea).
References
- C. Dong, C. C. Loy, K. He, and X. Tang, "Learning a Deep Convolutional Network for Image Super-Resolution," in Proc. ECCV, pp. 184-199, Sep. 6-12, 2014. DOI: https://doi.org/10.1007/978-3-319-10593-2
- C. Dong, C. C. Loy, and X. Tang, "Accelerating the Super-Resolution Convolutional Neural Network," in Proc. ECCV, pp. 391- 407, Oct. 11-14, 2016. DOI: https://doi.org/10.1007/978-3-319-46475-6
- J. K. Lee,J. Kim, and K. M. Lee, "Deeply-Recursive Convolutional Network for Image Super-Resolution," in Proc. CVPR 2016, pp. 1637-1645, June 27-30, 2016. DOI: 10.1109/CVPR.2016.181
- J. Kim, J. K. Lee, and K. M. Lee, "Accurate Image Super-Resolution Using Very Deep Convolutional Networks," in Proc. CVPR 2016, pp. 1646-1654, June 27-30, 2016. DOI: https://doi.org/10.1109/CVPR.2016.181
- B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee, "Enhanced Deep Residual Networks for Single Image Super-Resolution", in Proc. CVPR 2017 Workshops, pp. 136-144, July 21-26, 2017. DOI: https://doi.org/10.1109/CVPRW.2017.151
- Y. Park, S. Lee, B. Jeong, J. Yoon, "Joint Demosaicing and Denoising Based on a Variational Deep Image Prior Neural Network," Sensors, Vol. 20, No. 2970, pp. 1-14, May 2020. DOI: https://doi.org/10.3390/s20102970
- E. Kurniawan, Y. Park, and S. Lee, "Noise-Resistant Demosaicing with Deep Image Prior Network and Random RGBW Color Filter Array," Sensors, Vol. 22, No. 1767, pp. 1-13, Feb. 2022. DOI: https://doi.org/10.3390/s22051767