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 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.
Abstract (translated)
我们引入了StrCGAN（Stellar Cyclic GAN），这是一种生成模型，旨在提升低分辨率的天文摄影图像。我们的目标是重建高质量的真实地面观测水平的天体对象表示，这一任务由于小型望远镜观测如MobilTelesco数据集所具有的有限分辨率和质量而变得极具挑战性。传统的模型如CycleGAN为图像到图像转换提供了基础，但它们局限于二维映射，并且经常扭曲恒星和星系的形态。为了克服这些限制，我们在CycleGAN框架上引入了三项关键创新：三维卷积层用于捕捉体积空间相关性、多光谱融合以对齐光学和近红外（NIR）领域以及天体物理学正则化模块来保持恒星星形结构。来自覆盖从光学到NIR波段的多个任务全天调查的真实参考数据指导训练过程，确保重建在不同光谱带之间的一致性。这些组件共同使StrCGAN能够生成不仅视觉上更加清晰、而且物理一致性更高的重构图像，在天体物理学图像增强的任务中超越了标准GAN模型的表现。
URL
https://arxiv.org/abs/2509.19805
