神经盲反卷积用于重建高分辨率地面太阳观测
Neural blind deconvolution to reconstruct high-resolution ground-based solar observations
AI总结：
本文提出基于物理指导神经网络的神经盲反卷积方法，用于高分辨率太阳图像重建，实现了前所未有的空间分辨率和小尺度太阳特征的重建。
摘要：
地面太阳观测能够为太阳低层大气提供前所未有的空间、光谱和时间分辨率，但地球湍流大气对观测结果造成了显著限制，需要先进的事后图像重建技术。目前最先进的重建方法是将短曝光帧的 burst 重建为单个观测。这些技术的局限性源于对退化观测的点扩散函数（PSF）信息稀疏，从而影响观测质量和重建质量。我们开发了一种新的图像重建方法，以从短曝光图像 burst 中实现前所未有的空间分辨率。这可以提供高质量的重建，从而推进从太阳光球到色球的最小空间尺度研究。在本研究中，我们提出了一种基于物理指导神经网络的高分辨率太阳图像重建新方法。在训练过程中，神经网络将坐标点直接映射到其对应的强度值，同时更新 PSF 参数。该方法将神经网络中的真实物体与估计的 PSF 进行卷积，并通过最小化合成与真实短曝光图像 burst 之间的损失来优化网络。这种方法能够同时估计退化 PSF 和真实的高分辨率强度分布。我们通过从辐射磁流体动力学模拟中衍生出的合成强度数据演示了该方法，并将其应用于 GREGOR 和 DKIST 的高分辨率观测。我们的结果表明，该方法能够重建出超越现有最先进的重建方法的微小太阳特征。通过这种方法，我们为未来空间变化的 PSF 奠定了基础。
英文摘要：
Ground-based solar observations enable unprecedented spatial, spectral, and temporal resolution of the lower solar atmosphere, yet Earths turbulent atmosphere imposes significant limitations, requiring advanced post-facto image reconstruction. State-of-the-art reconstruction methods are based on restoring a burst of short exposure frames to a single observation. Limitations of these techniques arise due to the sparse information about the atmospheric point spread function (PSF) that degrade the observations and consequently the quality of reconstructions. We develop a novel image reconstruction method to achieve unprecedented spatial resolution from short exposure image bursts. This can provide high-quality reconstructions and therefore advance the study of the smallest spatial scales from the solar photosphere to the chromosphere. In this study, we present a novel approach for high-resolution solar image reconstruction based on physics-informed neural networks. In the training process, the neural network maps coordinate points directly to their corresponding intensity values while simultaneously updating the PSF parameters. The method convolves the true object from the neural network with the estimated PSFs and optimizes the network by minimizing the loss between the synthesized and real short-exposure image burst. This approach enables the simultaneous estimation of both the degrading PSF and the real high-resolution intensity distribution. We demonstrate the method on synthetic intensity data derived from a radiative MHD simulation and apply it to high-resolution observations from GREGOR and DKIST. Our results demonstrate the ability to reconstruct small-scale solar features that exceed the reconstruction performance of state-of-the-art reconstruction methods. With this approach we lay the foundation for future spatially varying PSFs.
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