Titlebook: Deep Learning in Solar Astronomy; Long Xu,Yihua Yan,Xin Huang Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive l

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Introduction,tional neural network has been verified most efficient for processing image. To process time series input, like video, recurrent neural network, e.g., long short-term memory (LSTM), was developed, which was widely known for forecasting the future, e.g., event occurrence, physical parameter predictio

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江湖郎中

Deep Learning in Solar Astronomy978-981-19-2746-1Series ISSN 2191-5768 Series E-ISSN 2191-5776

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Organization

978-981-19-2745-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor

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https://doi.org/10.1007/978-3-662-37014-8 techniques. It has been particularly successful in computer vision, machine translation, speech recognition and natural language processing. Modern astronomy concerns a big data challenge owning to high-resolution, high-precision and high-cadence telescopes. The big data presents a great challenge

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https://doi.org/10.1007/978-3-662-01274-1ellite continuously record high-resolution and high-cadence full-disk solar images. These images are used for solar activity forecasting and statistical analysis. Usually, it is required to mine key information from full-disk images firstly. Then, over extracted information, one can establish classi

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https://doi.org/10.1007/978-3-642-53067-8ty of image generation which is more challenging than classification. In this chapter, several applications of deep learning in solar image enhancement, reconstruction and processing are presented, including image deconvolution of solar radioheliograph, desaturation of solar imaging, generating magn