SeeingGAN: Galactic image deblurring with deep learning for better morphological classification of galaxies. (arXiv:2103.09711v2 [astro-ph.IM] UPDATED)
<a href="http://arxiv.org/find/astro-ph/1/au:+Gan_F/0/1/0/all/0/1">Fang Kai Gan</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Bekki_K/0/1/0/all/0/1">Kenji Bekki</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Hashemizadeh_A/0/1/0/all/0/1">Abdolhosein Hashemizadeh</a>
Classification of galactic morphologies is a crucial task in galactic
astronomy, and identifying fine structures of galaxies (e.g., spiral arms,
bars, and clumps) is an essential ingredient in such a classification task.
However, seeing effects can cause images we obtain to appear blurry, making it
difficult for astronomers to derive galaxies’ physical properties and, in
particular, distant galaxies. Here, we present a method that converts blurred
images obtained by the ground-based Subaru Telescope into quasi Hubble Space
Telescope (HST) images via machine learning. Using an existing deep learning
method called generative adversarial networks (GANs), we can eliminate seeing
effects, effectively resulting in an image similar to an image taken by the
HST. Using multiple Subaru telescope image and HST telescope image pairs, we
demonstrate that our model can augment fine structures present in the blurred
images in aid for better and more precise galactic classification. Using our
first of its kind machine learning-based deblurring technique on space images,
we can obtain up to 18% improvement in terms of CW-SSIM (Complex Wavelet
Structural Similarity Index) score when comparing the Subaru-HST pair versus
SeeingGAN-HST pair. With this model, we can generate HST-like images from
relatively less capable telescopes, making space exploration more accessible to
the broader astronomy community. Furthermore, this model can be used not only
in professional morphological classification studies of galaxies but in all
citizen science for galaxy classifications.
Classification of galactic morphologies is a crucial task in galactic
astronomy, and identifying fine structures of galaxies (e.g., spiral arms,
bars, and clumps) is an essential ingredient in such a classification task.
However, seeing effects can cause images we obtain to appear blurry, making it
difficult for astronomers to derive galaxies’ physical properties and, in
particular, distant galaxies. Here, we present a method that converts blurred
images obtained by the ground-based Subaru Telescope into quasi Hubble Space
Telescope (HST) images via machine learning. Using an existing deep learning
method called generative adversarial networks (GANs), we can eliminate seeing
effects, effectively resulting in an image similar to an image taken by the
HST. Using multiple Subaru telescope image and HST telescope image pairs, we
demonstrate that our model can augment fine structures present in the blurred
images in aid for better and more precise galactic classification. Using our
first of its kind machine learning-based deblurring technique on space images,
we can obtain up to 18% improvement in terms of CW-SSIM (Complex Wavelet
Structural Similarity Index) score when comparing the Subaru-HST pair versus
SeeingGAN-HST pair. With this model, we can generate HST-like images from
relatively less capable telescopes, making space exploration more accessible to
the broader astronomy community. Furthermore, this model can be used not only
in professional morphological classification studies of galaxies but in all
citizen science for galaxy classifications.
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