Super-resolution of sea surface temperature with convolutional neural network- and generative adversarial network-based methods

Abstract

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.;

Keywords

• convolutional neural network
• esrgan
• generative adversarial network
• rrdbnet
• single-image super-resolution