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    Using GAN Neural Networks for Super-Resolution Reconstruction of Temperature Fields

    Tao Li1, Zhiwei Jiang1,*, Rui Han2, Jinyue Xia3, Yongjun Ren4

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 941-956, 2023, DOI:10.32604/iasc.2023.029644

    Abstract A Generative Adversarial Neural (GAN) network is designed based on deep learning for the Super-Resolution (SR) reconstruction task of temperature fields (comparable to downscaling in the meteorological field), which is limited by the small number of ground stations and the sparse distribution of observations, resulting in a lack of fineness of data. To improve the network’s generalization performance, the residual structure, and batch normalization are used. Applying the nearest interpolation method to avoid over-smoothing of the climate element values instead of the conventional Bicubic interpolation in the computer vision field. Sub-pixel convolution is used instead… More >

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