仪器到仪器转换：仪器进步通过深度学习驱动太阳观测序列的恢复
Instrument-To-Instrument translation: Instrumental advances drive restoration of solar observation series via deep learning
AI总结：
提出一种通用的深度学习仪器到仪器转换（ITI）方法，通过在不同仪器的图像域间进行转换，使现有太阳观测数据集直接受益于最新仪器改进，成功应用于五个地基和天基太阳观测场景，生成了高分辨率、长时序且均匀的观测数据及磁场估计。
摘要：
天文仪器的持续改进为科学发现提供了基础。通常，这些改进仅具有向前的时间意义，而以前的观测并未受益于这一趋势。在此，我们提供了一种通用的深度学习方法，可在不同仪器的图像域之间进行转换（仪器到仪器转换；ITI）。通过将我们的方法应用于地基和天基太阳观测的五个不同应用，我们证明现有数据集可以直接从最新的仪器改进中受益。我们获得了：1）具有前所未有的空间分辨率的太阳全盘观测；2）24年的天基太阳极紫外（EUV）日冕和磁场观测的均匀数据序列；3）地基观测中大气退化的实时缓解；4）从1973年开始的均匀的地基H$α$观测序列；5）基于EUV图像的太阳远侧磁场估计。与同时进行的高质量观测的直接比较表明，我们的方法生成的图像在感知上相似，并匹配参考图像分布。
英文摘要：
The constant improvement of astronomical instrumentation provides the foundation for scientific discoveries. In general, these improvements have only implications forward in time, while previous observations do not benefit from this trend. Here we provide a general deep learning method that translates between image domains of different instruments (Instrument-To-Instrument translation; ITI). We demonstrate that the available data sets can directly profit from the most recent instrumental improvements, by applying our method to five different applications of ground- and space-based solar observations. We obtain 1) solar full-disk observations with unprecedented spatial resolution, 2) a homogeneous data series of 24 years of space-based observations of the solar EUV corona and magnetic field, 3) real-time mitigation of atmospheric degradations in ground-based observations, 4) a uniform series of ground-based H$α$ observations starting from 1973, 5) magnetic field estimates from the solar far-side based on EUV imagery. The direct comparison to simultaneous high-quality observations shows that our method produces images that are perceptually similar and match the reference image distribution.
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