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The volume of data being collected in solar astronomy has
exponentially increased over the past decade and we will be
entering the age of petabyte solar data. Deep learning has been an
invaluable tool exploited to efficiently extract key information
from the massive solar observation data, to solve the tasks of data
archiving/classification, object detection and recognition.
Astronomical study starts with imaging from recorded raw data,
followed by image processing, such as image reconstruction,
inpainting and generation, to enhance imaging quality. We study
deep learning for solar image processing. First, image
deconvolution is investigated for synthesis aperture imaging.
Second, image inpainting is explored to repair over-saturated solar
image due to light intensity beyond threshold of optical lens.
Third, image translation among UV/EUV observation of the
chromosphere/corona, Ha observation of the chromosphere and
magnetogram of the photosphere is realized by using GAN, exhibiting
powerful image domain transfer ability among multiple wavebands and
different observation devices. It can compensate the lack of
observation time or waveband. In addition, time series model, e.g.,
LSTM, is exploited to forecast solar burst and solar activity
indices. This book presents a comprehensive overview of the deep
learning applications in solar astronomy. It is suitable for the
students and young researchers who are major in astronomy and
computer science, especially interdisciplinary research of them.
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Nagreisiger
Leon van Nierop
Paperback
R240
Discovery Miles 2 400
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