MEM_GE: A New Maximum Entropy Method for Image Reconstruction from Solar X-Ray Visibilities
MEM_GE: A New Maximum Entropy Method for Image Reconstruction from Solar X-Ray Visibilities
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DOI：
10.3847/1538-4357/ab8637
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发表时间：
2020-05-01
影响因子：
4.9
通讯作者：
Benvenuto, Federico
中科院分区：
文献类型：
--
作者：
Massa, Paolo;Schwartz, Richard;Benvenuto, Federico
最大熵是一种图像重建方法，旨在对稀疏占据的视场进行成像，因此它在实现超分辨率效果方面特别有效。尽管该方法广泛用于图像反卷积，但该方法已在射电天文学中被制定用于分析空间频域中的观测结果，并且已实现交互式数据语言代码用于从太阳 X 射线傅立叶数据重建图像。然而，此代码依赖于最大熵方法解决的约束优化问题的非凸公式，这有时会导致以非物理收缩效应为特征的不可靠重建。本文介绍了一种基于凸函数约束最小化的最大熵的新方法。对于鲁文·拉马蒂高能太阳光谱成像仪 (RHESSI) 记录的观测结果，生成的代码提供了与先前算法相同的超分辨率效果，同时在该代码产生非物理重建时也能正常工作。我们还通过使用模拟太阳轨道飞行器中 X 射线成像光谱仪/望远镜 (STIX) 观测的合成数据测试算法来获得结果。新代码可在 Solar Software (SSW) 树的 HESSI 文件夹中找到。
Maximum Entropy is an image reconstruction method conceived to image a sparsely occupied field of view, therefore it is particularly effective at achieving super-resolution effects. Although widely used in image deconvolution, this method has been formulated in radio astronomy for the analysis of observations in the spatial frequency domain, and an Interactive Data Language code has been implemented for image reconstruction from solar X-ray Fourier data. However, this code relies on a non-convex formulation of the constrained optimization problem addressed by the Maximum Entropy approach, and this sometimes results in unreliable reconstructions characterized by unphysical shrinking effects. This paper introduces a new approach to Maximum Entropy based on the constrained minimization of a convex functional. In the case of observations recorded by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI), the resulting code provides the same super-resolution effects of the previous algorithm, while also working properly when that code produces unphysical reconstructions. We also obtain results via testing the algorithm with synthetic data simulating observations of the Spectrometer/Telescope for Imaging X-rays (STIX) in Solar Orbiter. The new code is available in the HESSI folder of the Solar SoftWare (SSW) tree.