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arXiv 1709.09458astro-ph.IM
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太阳成像的图像质量测量方法综述

Review of image quality measures for solar imaging

Adam Popowicz, Krystian Radlak, Krzysztof Bernacki, Valeri Orlov

摘要:

由于地球大气湍流的存在,从地面观测太阳光球遇到了重大问题。在应用图像重建技术之前,必须仔细选择在最有利的大气条件下获得的帧(所谓的幸运帧)。然而,包含复杂的光球结构的图像的质量的估计是不是一个微不足道的任务,并在夜间幸运成像观测应用的标准例程是不适用的。在本文中,我们评估了36种专门用于评估图像质量的方法,这些方法在过去40年的丰富文献中提出。我们比较它们的有效性模拟太阳观测的活动区域和颗粒补丁,使用参考数据获得的太阳光学望远镜的Hindoe卫星。为了创建受已知程度的大气退化影响的图像,我们采用了随机波矢量方法,该方法忠实地模拟了所有的视宁度特征。结果提供了有用的信息的方法的性能取决于平均看到的条件表示的比率的望远镜的孔径的弗里德参数,$D/r_0$。比较确定了三种方法供观察者考虑:Helmli和Scherer平均值,中值滤波梯度相似性和离散余弦变换能量比。虽然第一个需要更少的计算工作量,并且几乎可以在任何大气条件下有效使用,但第二个在良好的视宁度($D/r_0<4$)方面显示出其优越性。最后一种方法主要用于强模糊图像的后处理。

英文摘要:

The observations of solar photosphere from the ground encounter significant problems due to the presence of Earth's turbulent atmosphere. Prior to applying image reconstruction techniques, the frames obtained in most favorable atmospheric conditions (so-called lucky frames) have to be carefully selected. However, the estimation of the quality of images containing complex photospheric structures is not a trivial task and the standard routines applied in night-time Lucky Imaging observations are not applicable. In this paper we evaluate 36 methods dedicated for the assessment of image quality which were presented in the rich literature over last 40 years. We compare their effectiveness on simulated solar observations of both active regions and granulation patches, using reference data obtained by the Solar Optical Telescope on the Hindoe satellite. To create the images affected by a known degree of atmospheric degradation, we employ the Random Wave Vector method which faithfully models all the seeing characteristics. The results provide useful information about the methods performance depending on the average seeing conditions expressed by the ratio of the telescope's aperture to the Fried parameter, $D/r_0$. The comparison identifies three methods for consideration by observers: Helmli and Scherer's Mean, Median Filter Gradient Similarity, and Discrete Cosine Transform Energy Ratio. While the first one requires less computational effort and can be used effectively virtually in any atmospherics conditions, the second one shows its superiority at good seeing ($D/r_0<4$). The last one should be considered mainly for the post-processing of strongly blurred images.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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