用深度学习将100张短曝光图像实时合成高清太阳观测图
Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations
- 采用无配对图像翻译，用退化图像和斑点重建作为参考训练
- 在强湍流下仍保持高感知质量，全量图像融合效果最佳
- 适合需要实时高分辨率太阳成像的天文观测场景
大口径地面太阳望远镜可以前所未有的细节解析太阳大气，但受地球湍流大气影响，需进行事后图像修正。现有基于短曝光序列的重建方法在强湍流下表现受限，且计算成本高。本文提出一种深度学习方法，可在实时条件下将100张短曝光图像合成为一张高质量图像。模型采用无配对图像到图像转换，以退化图像序列和斑点重建结果作为参考进行训练，提升了鲁棒性和泛化能力。评估显示，该方法在感知质量上优于传统方法，尤其当斑点重建出现伪影时优势明显。通过不同数量图像的实验验证，本方法能高效利用全部图像信息，在完整图像序列输入时达到最佳重建效果。
原文摘要 · Abstract (English)
Large aperture ground based solar telescopes allow the solar atmosphere to be resolved in unprecedented detail. However, observations are limited by Earths turbulent atmosphere, requiring post image corrections. Current reconstruction methods using short exposure bursts face challenges with strong turbulence and high computational costs. We introduce a deep learning approach that reconstructs 100 short exposure images into one high quality image in real time. Using unpaired image to image translation, our model is trained on degraded bursts with speckle reconstructions as references, improving robustness and generalization. Our method shows an improved robustness in terms of perceptual quality, especially when speckle reconstructions show artifacts. An evaluation with a varying number of images per burst demonstrates that our method makes efficient use of the combined image information and achieves the best reconstructions when provided with the full image burst.
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