  Image-to-image translation with Deep Learning neural networks, particularly
with Generative Adversarial Networks (GANs), is one of the most powerful
methods for simulating astronomical images. However, current work is limited to
utilizing paired images with supervised translation, and there has been rare
discussion on reconstructing noise background that encodes instrumental and
observational effects. These limitations might be harmful for subsequent
scientific applications in astrophysics. Therefore, we aim to develop methods
for using unpaired images and preserving noise characteristics in image
translation. In this work, we propose a two-way image translation model using
GANs that exploits both paired and unpaired images in a semi-supervised manner,
and introduce a noise emulating module that is able to learn and reconstruct
noise characterized by high-frequency features. By experimenting on multi-band
galaxy images from the Sloan Digital Sky Survey (SDSS) and the Canada France
Hawaii Telescope Legacy Survey (CFHT), we show that our method recovers global
and local properties effectively and outperforms benchmark image translation
models. To our best knowledge, this work is the first attempt to apply
semi-supervised methods and noise reconstruction techniques in astrophysical
studies.
