基于卷积神经网络的太阳图像去噪
Solar image denoising with convolutional neural networks
摘要：
太阳色球层的拓扑结构和动力学特性受磁场的影响很大。磁场可以通过分析谱线的偏振观测来推断。然而，由色球磁场引起的偏振信号特别微弱，并且在大多数情况下非常接近当前仪器的检测极限。正因为如此，只有少数观测研究成功地重建了色球层中磁场矢量的三个分量。传统上，观测值的信噪比通过执行时间平均或空间平均来提高，但是在这两种情况下，都丢失了一些信息。更先进的技术，如主成分分析，也被用来利用稀疏的观测在频谱方向。在本研究中，我们建议使用深度学习技术，利用观测的空间相干性来减少噪声。我们设计了一个神经网络，它能够在复杂的噪声污染（包括仪器伪影和非线性后处理）下恢复弱信号。网络的训练是在没有干净信号的先验知识或噪声或其他损坏的显式统计特征的情况下进行的。我们只使用与生成模型相同的观察结果。该方法的性能证明，合成实验和真实数据。我们展示了在目前的望远镜，如瑞典1米太阳望远镜中获得的典型信号的改善的例子。该方法无论在哪条谱线上或采用谱采样都能很好地恢复微弱信号。特别适用于波长采样较少的情况。
英文摘要：
The topology and dynamics of the solar chromosphere are greatly affected by the presence of magnetic fields. The magnetic field can be inferred by analyzing polarimetric observations of spectral lines. Polarimetric signals induced by chromospheric magnetic fields are, however, particularly weak, and in most cases very close to the detection limit of current instrumentation. Because of this, there are only few observational studies that have successfully reconstructed the three components of the magnetic field vector in the chromosphere. Traditionally, the signal-to-noise ratio of observations has been improved by performing time-averages or spatial averages, but in both cases, some information is lost. More advanced techniques, like principal-component-analysis, have also been employed to take advantage of the sparsity of the observations in the spectral direction. In the present study, we propose to use the spatial coherence of the observations to reduce the noise using deep-learning techniques. We design a neural network that is capable of recovering weak signals under a complex noise corruption (including instrumental artifacts and non-linear post-processing). The training of the network is carried out without a priori knowledge of the clean signals, or an explicit statistical characterization of the noise or other corruption. We only use the same observations as our generative model. The performance of this method is demonstrated on both, synthetic experiments and real data. We show examples of the improvement in typical signals obtained in current telescopes such as the Swedish 1-meter Solar Telescope. The presented method can recover weak signals equally well no matter on what spectral line or spectral sampling is used. It is especially suitable for cases when the wavelength sampling is scarce.
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