Acknowledgement
이 논문은 2021년 정부(방위사업청)의 재원으로 국방기술진흥연구소의 지원을 받아 수행된 연구임(KRIT-CT-21-032)
References
- 이우석; 이강수; 이호철. 곤충 눈 모사 설계 위한 가시화 기법의 적용 방법 연구. 한국생산제조학회 학술발표대회논문집 275-275, 2015.
- KIRSCHFELD, K. The resolution of lens and compound eyes. In: Neural principles in vision. Berlin, Heidelberg: Springer Berlin Heidelberg, p. 354-370, 1976.
- 김동건, et al. 초박형 라이트필드 카메라의 실시간 분해능 향상 알고리즘 개발. Journal of the Korea Computer Graphics Society, 27.3: 25-29, 2021.
- SHIN, Do-Kyung, et al. No-Reference Image Quality Assessment Based on Statistical Characterization of Frequency Bands. The Society of Convergence Knowledge Transactions, 10.1: 81-101, 2022
- 신재우, et al. 딥러닝 기반 손상된 흑백 얼굴 사진 컬러 복원. Journal of the Korea Computer Graphics Society, 24.2: 1-9, 2018. https://doi.org/10.15701/kcgs.2018.24.2.1
- LEDIG, Christian, et al. Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition p. 4681-4690, 2017.
- Wang, Xintao, et al. "Esrgan: Enhanced super-resolution generative adversarial networks." Proceedings of the European conference on computer vision (ECCV) workshops. 2018.
- SHARMA, Prith, et al. Image Enhancement using ESRGAN for CNN based X-Ray Classification. In: 2022 5th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, p. 1965-1969, 2022.
- SONG, Chengkun, et al. Low resolution face recognition system based on ESRGAN. In: 2021 3rd International Conference on Applied Machine Learning (ICAML). IEEE, p. 76-79, 2021.
- Wang, Manyu, et al. "A new image denoising method based on Gaussian filter." 2014 International Conference on information science, electronics and electrical engineering. Vol. 1. IEEE, 2014.
- ZHANG, Zhengyou. A flexible new technique for camera calibration. IEEE Transactions on pattern analysis and machine intelligence, 22.11: 1330-1334, 2000. https://doi.org/10.1109/34.888718
- Li, Zhengguo, et al. "Detail-enhanced multi-scale exposure fusion." IEEE Transactions on Image processing 26.3, 1243-1252, 2017. https://doi.org/10.1109/TIP.2017.2651366
- Mertens, Tom, Jan Kautz, and Frank Van Reeth. "Exposure fusion: A simple and practical alternative to high dynamic range photography." Computer graphics forum. Vol. 28. No. 1. Oxford, UK: Blackwell Publishing Ltd, 2009.
- Pizer, Stephen M., et al. "Adaptive histogram equalization and its variations." Computer vision, graphics, and image processing 39.3, 355-368, 1987 https://doi.org/10.1016/S0734-189X(87)80186-X
- Wang, Xintao, et al. "Real-esrgan: Training real-world blind super-resolution with pure synthetic data." Proceedings of the IEEE/CVF international conference on computer vision. 2021.
- Mittal, Anish, Anush Krishna Moorthy, and Alan Conrad Bovik. "No-reference image quality assessment in the spatial domain." IEEE Transactions on image processing 21.12, 4695-4708, 2012. https://doi.org/10.1109/TIP.2012.2214050
- Ward, Chris M., et al. "Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN)." Applications of Digital Image Processing XL. Vol. 10396. SPIE, 2017.
- Zhang, Wenlong, et al. "Ranksrgan: Super resolution generative adversarial networks with learning to rank." IEEE Transactions on Pattern Analysis and Machine Intelligence 44.10, 7149-7166, 2021. https://doi.org/10.1109/TPAMI.2021.3096327
- Tao, Yu, and Jan-Peter Muller. "Super-resolution restoration of MISR images using the UCL MAGiGAN system." Remote Sensing 11.1, 52, 2018.