Acknowledgement
본 연구는 산업통상자원부와 한국산업기술진흥원의 "지역혁신클러스터육성사업(R&D, P0004797)"으로 수행된 연구결과 입니다.
References
- C. Ledig, et al., "Photo-realistic single image superresolution using a generative adversarial network," Proc. of CVPR, pp. 105-114, 2017.
- G. Hinton, et al., "Distilling the knowledge in a neural network," Proc. of NIPS, 2014.
- Y. Zhang, et al.,"Deep mutual learning," Proc. of CVPR, pp. 4320-4328, 2018.
- A. Romero, et al., "FitNets: hints for thin deep nets," Proc. of ICLR, 2015.
- N. Komodakis and S. Zagoruyko, "Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer," Proc. of ICLR, 2017.
- L. Zhang, J. Song, A. Gao, J. Chen, C. Bao, and K. Ma, "Be your own teacher: improve the performance of convolutional neural networks via self distillation," Proc. of ICCV, pp. 3713-3722, 2019.
- Z. He, et al., "Fakd: feature-affinity based knowledge distillation for efficient image super-resolution," Proc. of ICIP, pp. 518-522, 2020.
- C. Dong, C. C. Loy, K. He, and X. Tang, "Image super-resolution using deep convolutional networks," Proc. of ECCV, pp. 184-199, 2014.
- I. J. Goodfellow, et al., "Generative adversarial networks," arXiv preprint arXiv:1406.2661, 2014.
- K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint, arXiv:1409.1556, 2014.
- X. Wang, et al., "ESRGAN: enhanced super resolution generative adversarial networks," Proc. of ECCV, pp. 63-79, 2018.
- Y. Choi and H. Park, "Improving ESRGAN with an additional image quality loss," Multimedia Tools and Applications, 2022.
- E. Agustsson and R. Timofte, "Ntire 2017 challenge on single image super-resolution: dataset and study," Proc. of CVPRW, pp. 126-135, 2017.
- M. Bevilacqua, et al., "Low complexity single image super-resolution based on nonnegative neighbor embedding," Proc. BMVC, 2012.
- R. Zeyde, et al., "On single image scale-up using sparse-representations," Proc. of Int. Conf. on Curves and Surfaces, pp. 711-730, 2010.
- D. Martin, et al., "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics," Proc. ICCV, vol. 2, pp. 416-423, 2001.
- J.-B. Huang, et al., "Single image super-resolution from transformed self-exemplars," Proc. of CVPR, pp. 5197-5206, 2015.
- C. Ma, et al., "Learning a no-Reference quality metric for single-image super-resolution," CVIU, vol. 158, pp. 1-16, 2017.
- A. Mittal, et al., "Making a completely blind image quality analyzer," IEEE Signal Processing Letters, vol. 20, no. 3, pp. 209-212, 2013. https://doi.org/10.1109/LSP.2012.2227726