用多光谱融合与天体物理正则化提升小望远镜星图清晰度
StrCGAN: A Generative Framework for Stellar Image Restoration
- 引入多光谱融合与天体物理正则化模块增强星体形态保持能力
- 在MobilTelesco数据集上重建图像更锐利，视觉质量优于标准GAN
- 适合天文影像修复、业余望远镜图像增强等场景
我们提出StrCGAN（Stellar Cyclic GAN），一种用于提升低分辨率天文摄影图像的生成模型。目标是重建接近真实高保真度的恒星图像，该任务因小望远镜观测（如MobilTelesco数据集）分辨率和质量有限而极具挑战。传统CycleGAN虽能实现图像到图像转换，但常扭曲星体形态，生成结果失真。为此，我们在CycleGAN基础上引入两项关键改进：多光谱融合以对齐光学与近红外（NIR）波段，以及天体物理正则化模块以保持星体结构。训练过程由覆盖光学至NIR波段的多任务全天巡天数据作为真实参考，确保跨波段一致性。实验表明，StrCGAN生成的重构图像在视觉上更锐利，在天体图像增强任务中显著优于标准GAN模型。
原文摘要 · Abstract (English)
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.
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