StrCGAN: A Generative Framework for Stellar Image Restoration
This work addresses the challenge of improving image quality for small-telescope observations in astronomy, which is an incremental advancement over existing methods.
The authors tackled the problem of enhancing low-resolution astrophotography images by developing StrCGAN, a generative model that reconstructs high-fidelity representations of celestial objects, achieving visually sharper and physically consistent results compared to standard GAN models.
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 celestial 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 are restricted to 2D mappings and often distort the morphology of stars and galaxies. To overcome these limitations, we extend the CycleGAN framework with three key innovations: 3D convolutional layers to capture volumetric spatial correlations, 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 not only visually sharper but also physically consistent, outperforming standard GAN models in the task of astrophysical image enhancement.