Acknowledgement
이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단(No.2022R1A4A5028840)의 지원과 환경부의 재원으로 한국환경산업기술원(RS-2023-00218973)의 지원을 받아 수행된 연구임.
References
- Ballard, T., and Erinjippurath, G. (2020). "FireSRnet: Geoscience-driven super-resolution of future fire risk from climate change." arXiv Preprint, arXiv:2011.12353.
- Ballard, T., and Erinjippurath, G. (2022). "Contrastive learning for climate model bias correction and super-resolution." arXiv Preprint, arXiv:2211.07555.
- Bashir, S.M.A., Wang, Y., Khan, M., and Niu, Y. (2021). "A comprehensive review of deep learning-based single image super-resolution." PeerJ Computer Science, Vol. 7, e621.
- Caballero, J., Ledig, C., Aitken, A., Acosta, A., Totz, J., Wang, Z., and Shi, W. (2017). "Real-time video super-resolution with spatio-temporal networks and motion compensation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, U.S., pp. 4778-4787.
- Cheng, J., Kuang, Q., Shen, C., Liu, J., Tan, X., and Liu, W. (2020). "ResLap: Generating high-resolution climate prediction through image super-resolution." IEEE Access, Vol. 8, pp. 39623-39634. https://doi.org/10.1109/ACCESS.2020.2974785
- Cooley, A., and Chang, H. (2017). "Precipitation intensity trend detection using hourly and daily observations in Portland, Oregon." Climate, Vol. 5, No. 1, 10.
- Dai, T., Cai, J., Zhang, Y., Xia, S.-T., and Zhang, L. (2019). "Second-order attention network for single image super-resolution." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, pp. 1106511074.
- Dong, C., Loy, C.C., He, K., and Tang, X. (2014). "Learning a deep convolutional network for image super-resolution." Computer Vision - ECCV 2014: 13th European Conference, Zurich, Switzerland, pp. 184-199.
- Galar, M., Sesma, R., Ayala, C., Albizua, L., and Aranda, C. (2020). "Super-resolution of Sentinel-2 images using convolutional neural networks and real ground truth data." Remote Sensing, MDPI, Vol. 12, No. 18, 2941.
- Guidolin, M., Chen, A.S., Ghimire, B., Keedwell, E.C., Djordjevic, S., and Savic, D.A. (2016). "A weighted cellular automata 2D inundation model for rapid flood analysis." Environmental Modelling & Software, Vol. 84, pp. 378-394.
- Guo, Z., Leitao, J.P., Simoes, N.E., and Moosavi, V. (2021). "Data-driven flood emulation: Speeding up urban flood predictions by deep convolutional neural networks." Journal of Flood Risk Management, Vol. 14, No. 1, e12684.
- He, J., Zhang, L., Xiao, T., Wang, H., and Luo, H. (2023). "Deep learning enables super-resolution hydrodynamic flooding process modeling under spatiotemporally varying rainstorms." Water Research, Vol. 239, 120057.
- Hwang, S.-G., and Lee, J.-H. (2023). "Super-resolution technique of underwater image based on lightweight convolutional neural network for marine accident cause analysis." Journal of Korean Institute of Intelligent Systems, Vol. 33, No. 2, pp. 127-132. https://doi.org/10.5391/JKIIS.2023.33.2.127
- Jia, Y., Ge, Y., Chen, Y., Li, S., Heuvelink, G.B.M., and Ling, F. (2019). "Super-resolution land cover mapping based on the convolutional neural network." Remote Sensing, MDPI, Vol. 11, No. 15, 1815.
- Kim, J., Lee, J.K., and Lee, K.M. (2016). "Deeply-recursive convolutional network for image super-resolution." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, U.S., pp. 1637-1645.
- Kwon, O.-S. (2023). "Vehicle detection algorithm using super resolution based on deep residual dense block for remote sensing images." Journal of Broadcast Engineering, Vol. 28, No. 1, pp. 124-131. https://doi.org/10.5909/JBE.2023.28.1.124
- Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., and Shi, W. (2017). "Photo-realistic single image super-resolution using a generative adversarial network." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, U.S., pp. 4681-4690.
- Lee, S., Nakagawa, H., Kawaike, K., and Zhang, H. (2016). "Urban inundation simulation considering road network and building configurations." Journal of Flood Risk Management, Vol. 9, No. 3, pp. 224-233. https://doi.org/10.1111/jfr3.12165
- Lim, B., Son, S., Kim, H., Nah, S., and Lee, K.M. (2017). "Enhanced deep residual networks for single image super-resolution." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, U.S., pp. 136-144.
- Lombana, L., and Martinez-Grana, A. (2022). "A flood mapping method for land use management in small-size water bodies: Validation of spectral indexes and a machine learning technique." Agronomy, MDPI, Vol. 12, No. 6, 1280.
- Mei, Y., Fan, Y., Zhou, Y., Huang, L., Huang, T.S., and Shi, H. (2020). "Image super-resolution with cross-scale non-local attention and exhaustive self-exemplars mining." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, U.S., pp. 5690-5699.
- Moreno-Rodenas, A.M., Bellos, V., Langeveld, J.G., and Clemens, F.H.L.R. (2018). "A dynamic emulator for physically based flow simulators under varying rainfall and parametric conditions." Water Research, Vol. 142, pp. 512-527. https://doi.org/10.1016/j.watres.2018.06.011
- Noh, S.J., Lee, J.-H., Lee, S., and Seo, D.-J. (2019). "Retrospective dynamic inundation mapping of hurricane harvey flooding in the Houston Metropolitan Area using high-resolution modeling and high-performance computing." Water, Vol. 11, No. 3, 597.
- Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D.J., and Norouzi, M. (2021). "Image super-resolution via iterative refinement." IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, No. 4, pp. 4713-4726.
- Sara, U., Akter, M., and Uddin, M.S. (2019). "Image quality assessment through FSIM, SSIM, MSE and PSNR - A comparative study." Journal of Computer and Communications, Vol. 7, No. 3, pp. 8-18. https://doi.org/10.4236/jcc.2019.73002
- Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2016). "Inception-v4, Inception-ResNet and the impact of residual connections on learning." Proceedings of the AAAI Conference on Artificial Intelligence, Phoenix, AZ, U.S., Vol. 31, No. 1, pp. 4278-4284.
- Wang, X., Yi, J., Guo, J., Song, Y., Lyu, J., Xu, J., Yan, W., Zhao, J., Cai, Q., and Min, H. (2022). "A review of image super-resolution approaches based on deep learning and applications in remote sensing." Remote Sensing, MDPI, Vol. 14, No. 21, 5423.
- Wang, Y., Chen, A.S., Fu, G., Djordjevic, S., Zhang, C., and Savic, D.A. (2018). "An integrated framework for high-resolution urban flood modelling considering multiple information sources and urban features." Environmental Modelling & Software, Vol. 107, pp. 85-95. https://doi.org/10.1016/j.envsoft.2018.06.010
- Wang, Z., Bovik, A., Sheikh, H., and Simoncelli, E. (2004). "Image quality assessment: From error visibility to structural similarity." IEEE Transactions on Image Processing, Vol. 13, No. 4, pp. 600-612. https://doi.org/10.1109/TIP.2003.819861
- Zha, L., Yang, Y., Lai, Z., Zhang, Z., and Wen, J. (2021). "A lightweight dense connected approach with attention on single image super-resolution." Electronics, MDPI, Vol. 10, No. 11, 1234.