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

Atmosphere(2019)SCI 4区

Chengdu Univ Informat Technol

Cited 14|Views17
Abstract
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.
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weather radar,image super-resolution,generative adversarial networks,deep learning
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要点】:本文提出利用生成对抗网络(GAN)进行天气雷达回波图像的超分辨率重建,以改善重建图像的纹理细节,并提高峰值信噪比(PSNR)和结构相似性指数(SSIM)。

方法】:通过利用GAN的能力,将超分辨率解决方案推向自然图像流形,从而提高天气雷达回波图像的重建性能。

实验】:实验使用了真实的天气雷达回波数据,并将GAN-based方法的重建性能与迭代反向投影(IBP)和非局部自相似性稀疏表示(NSSR)等传统方法进行了比较,结果显示GAN-based方法在感知质量和PSNR/SSIM指标上均有显著提升。