Galaxy Image Translation with Semi-supervised Noise-reconstructed Generative Adversarial Networks

TL;DR

This paper introduces a semi-supervised GAN-based model for galaxy image translation that preserves noise characteristics, improving the realism and scientific utility of translated astronomical images.

Contribution

It develops a novel semi-supervised two-way GAN model with a noise reconstruction module for astrophysical image translation using both paired and unpaired data.

Findings

Outperforms benchmark models in property preservation

Successfully reconstructs noise features in galaxy images

Effective on multi-band galaxy datasets from SDSS and CFHT

Abstract

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.