Super-resolution of sea surface temperature with convolutional neural network- and generative adversarial network-based methods
| Autor: | Tomoki Izumi, Motoki Amagasaki, Kei Ishida, Masato Kiyama |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Journal of Water and Climate Change, Vol 13, Iss 4, Pp 1673-1683 (2022) |
| Druh dokumentu: | article |
| ISSN: | 2040-2244 2408-9354 |
| DOI: | 10.2166/wcc.2022.291 |
| Popis: | In this paper, we perform the super-resolution of sea surface temperature data with the enhanced super-resolution generative adversarial network (ESRGAN), which is a deep neural network-based single-image super-resolution (SISR) method that uses a generative adversarial network (GAN). We generate high-quality super-resolution data with ESRGAN and with the super-resolution convolutional neural network (SRCNN) and residual-in-residual dense block network (RRDBNet) methods, which are based on convolutional neural networks (CNNs). The images generated with these methods are compared with high-resolution optimum interpolation sea surface temperature (OISST) data using root mean square error (RMSE), learned perceptual image patch similarity (LPIPS), and perceptual index (PI) evaluation methods. RRDBNet has a better RMSE than SRCNN and ESRGAN. However, CNN-based SISR methods do not provide a faithful representation of the ocean currents of OISST. ESRGAN has a better LPIPS and PI than CNN-based methods and can represent the complex distribution of ocean currents. HIGHLIGHTS RRDBNet has a better RMSE than SRCNN and ESRGAN on super-resolution of sea surface temperature data.; ESRGAN has a better LPIPS and PI than CNN-based methods and can represent the complex distribution of ocean currents.; CNNs cannot interpolate the missing information, but GANs have better results for these parts.; |
| Databáze: | Directory of Open Access Journals |
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