Super-resolution of sea surface temperature with convolutional neural network- and generative adversarial network-based methods
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 hi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IWA Publishing
2022-04-01
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| Series: | Journal of Water and Climate Change |
| Subjects: | |
| Online Access: | http://jwcc.iwaponline.com/content/13/4/1673 |
| Summary: | 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.; |
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| ISSN: | 2040-2244 2408-9354 |