# StrCGAN: A Generative Framework for Stellar Image Restoration

arXiv: https://arxiv.org/abs/2509.19805 · HTML: https://mlsanity.com/paper/2509.19805/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 38 references that are in the map.
- First posted on arXiv 24 Sep 2025.

## Builds on (most cited first, 21 of 21)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Differentiable Augmentation for Data-Efficient GAN Training](https://mlsanity.com/paper/2006.10738/) (2020)
- [SinGAN: Learning a Generative Model from a Single Natural Image](https://mlsanity.com/paper/1905.01164/) (2019)
- [Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data](https://mlsanity.com/paper/1802.10151/) (2018)
- [GP-GAN: Towards Realistic High-Resolution Image Blending](https://mlsanity.com/paper/1703.07195/) (2017)
- [Stacked Generative Adversarial Networks](https://mlsanity.com/paper/1612.04357/) (2016)
- [Multi-Frame GAN: Image Enhancement for Stereo Visual Odometry in Low Light](https://mlsanity.com/paper/1910.06632/) (2019)
- [CharacterGAN: Few-Shot Keypoint Character Animation and Reposing](https://mlsanity.com/paper/2102.03141/) (2021)
- [Radio astronomical images object detection and segmentation: A benchmark on deep learning methods](https://mlsanity.com/paper/2303.04506/) (2023)
- [Seg-CycleGAN : SAR-to-optical image translation guided by a downstream task](https://mlsanity.com/paper/2408.05777/) (2024)
- [Generative Adversarial Networks for Astronomical Images Generation](https://mlsanity.com/paper/2111.11578/) (2021)
- [Astrophotography turbulence mitigation via generative models](https://mlsanity.com/paper/2506.02981/) (2025)
- [Benchmarking Deep Learning-Based Object Detection Models on Feature Deficient Astrophotography Imagery Dataset](https://mlsanity.com/paper/2508.06537/) (2025)

## Abstract (arXiv)

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.