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.
