StrCGAN: A Generative Framework for Stellar Image Restoration
Shantanusinh Parmar
我们介绍了StrCGAN(恒星循环GAN),这是一种旨在增强低分辨率天文摄影图像的生成模型。 我们的目标是重建天体的高保真地面真实性表示,由于小望远镜观测(如MobilTelesco数据集)的分辨率和质量有限,这项任务具有挑战性。 CycleGAN等传统模型为图像到图像的翻译提供了基础,但仅限于2D映射,并经常扭曲恒星和星系的形态。 为了克服这些限制,我们扩展了CycleGAN框架,具有三个关键创新:3D卷积层以捕获体积空间相关性,多光谱融合以对齐光学和近红外(NIR)域,以及天体物理正则化模块,以保存恒星形态。 跨越光学到NIR的多任务全天空调查的地面真相参考指导了训练过程,确保光谱波段的重建保持一致。 这些组件共同允许StrCGAN生成重建,这些重建不仅视觉更锐利,而且在物理上更一致,在天体物理图像增强的任务中优于标准GAN模型。
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 mo...