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Open AccessArticle

Generative Adversarial Networks Capabilities for Super-Resolution Reconstruction of Weather Radar Echo Images

1
College of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China
2
College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, China
3
CMA Key Laboratory of Atmospheric Sounding, Chengdu 610225, China
*
Author to whom correspondence should be addressed.
Atmosphere 2019, 10(9), 555; https://doi.org/10.3390/atmos10090555
Received: 17 July 2019 / Revised: 18 August 2019 / Accepted: 10 September 2019 / Published: 16 September 2019
(This article belongs to the Section Climatology and Meteorology)
Improving the resolution of degraded radar echo images of weather radar systems can aid severe weather forecasting and disaster prevention. Previous approaches to this problem include classical super-resolution (SR) algorithms such as iterative back-projection (IBP) and a recent nonlocal self-similarity sparse representation (NSSR) that exploits the data redundancy of radar echo data, etc. However, since radar echoes tend to have rich edge information and contour textures, the textural detail in the reconstructed echoes of traditional approaches is typically absent. Inspired by the recent advances of faster and deeper neural networks, especially the generative adversarial networks (GAN), which are capable of pushing SR solutions to the natural image manifold, we propose using GAN to tackle the problem of weather radar echo super-resolution to achieve better reconstruction performance (measured in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM)). Using authentic weather radar echo data, we present the experimental results and compare its reconstruction performance with the above-mentioned methods. The experimental results showed that the GAN-based method is capable of generating perceptually superior solutions while achieving higher PSNR/SSIM results. View Full-Text
Keywords: weather radar; image super-resolution; generative adversarial networks; deep learning weather radar; image super-resolution; generative adversarial networks; deep learning
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Chen, H.; Zhang, X.; Liu, Y.; Zeng, Q. Generative Adversarial Networks Capabilities for Super-Resolution Reconstruction of Weather Radar Echo Images. Atmosphere 2019, 10, 555.

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