标题： 基于深度学习的图像 burst 堆栈以重建基于地面的高分辨率太阳观测 显示英文标题

标题： Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations

摘要： 大口径地基太阳望远镜能够以前所未有的细节分辨率观测太阳大气。然而，观测受到地球湍流大气的限制，需要进行图像后处理校正。目前使用短曝光序列的重建方法在强湍流条件下面临挑战，并且计算成本较高。我们提出了一种深度学习方法，可以实时将100张短曝光图像重建为一张高质量图像。通过无配对图像到图像转换，我们的模型以退化序列作为训练数据，并以散斑重建作为参考，提高了鲁棒性和泛化能力。我们的方法在感知质量方面表现出更高的鲁棒性，尤其是在散斑重建出现伪影时。通过对每次曝光序列中不同数量图像的评估显示，我们的方法有效利用了组合图像信息，在提供完整图像序列时实现了最佳的重建效果。 显示英文摘要

摘要： 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.