StrCGAN: A Generative Framework for Stellar Image Restoration

Abstract

We introduce StrCGAN (Stellar Cyclic GAN), a generative model designed to enhance low-resolution astrophotography images. Our goal is to reconstruct high fidelity ground truth like representations of stellar objects, a task that is challenging due to the limited resolution and quality of small-telescope observations such as the MobilTelesco dataset. Traditional models such as CycleGAN provide a foundation for image to image translation but often distort the morphology of stars and produce barely resembling images. To overcome these limitations, we extend the CycleGAN framework with some key innovations: multi-spectral fusion to align optical and near infrared (NIR) domains, and astrophysical regularization modules to preserve stellar morphology. Ground truth references from multi-mission all sky surveys spanning optical to NIR guide the training process, ensuring that reconstructions remain consistent across spectral bands. Together, these components allow StrCGAN to generate reconstructions that are visually sharper outperforming standard GAN models in the task of astrophysical image enhancement.