Galaxy Image Translation with Semi-supervised Noise-reconstructed Generative Adversarial Networks
This work addresses limitations in simulating astronomical images for astrophysics applications, though it appears incremental as it builds on existing GAN-based translation methods.
The paper tackled the problem of image-to-image translation for astronomical images by developing a method that uses both paired and unpaired data in a semi-supervised manner and reconstructs noise characteristics, showing effective recovery of global and local properties and outperforming benchmark models.
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.