High-precision Multichannel Solar Image Registration Using Image Intensity
High-precision Multichannel Solar Image Registration Using Image Intensity
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DOI：
10.3847/1538-4365/ac7232
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Bo Liang;Xi Chen;Lan Yu;Song Feng;Yangfan Guo;W. Cao;W. Dai;Yunfei Yang;D. Yuan
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文献类型：
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作者：
Bo Liang;Xi Chen;Lan Yu;Song Feng;Yangfan Guo;W. Cao;W. Dai;Yunfei Yang;D. Yuan
使用不同仪器在不同通道观测到的太阳图像对于太阳活动的研究至关重要。然而，这些图像具有不同的视野，导致它们未对准。准确记录图像对于从多个角度研究太阳活动至关重要。图像配准被描述为从要配准的图像到参考图像的优化问题。在本文中，我们提出了一种新颖的从粗到细的太阳图像配准方法来配准多通道太阳图像。在粗配准步骤中，我们使用常规步骤梯度下降算法作为优化器来最大化归一化互相关度量。精细配准步骤使用 Powell-Brent 算法作为优化器，并将 Mattes 互信息相似度度量降至最低。我们选择了五对具有不同分辨率、旋转角度和位移的图像，将我们的结果与通过尺度不变特征变换和相位相关获得的结果进行比较和评估。这些图像是由大熊太阳天文台的 1.6 m 古德太阳望远镜和太阳动力学天文台的日震和磁成像仪观测到的。此外，我们使用互信息和注册时间标准来量化注册结果。结果表明，该方法不仅达到了较好的配准精度，而且具有较好的鲁棒性。同时，我们要强调的是，该方法也可以很好地用于时间序列太阳图像配准。
Solar images observed in different channels with different instruments are crucial to the study of solar activity. However, the images have different fields of view, causing them to be misaligned. It is essential to accurately register the images for studying solar activity from multiple perspectives. Image registration is described as an optimizing problem from an image to be registered to a reference image. In this paper, we proposed a novel coarse-to-fine solar image registration method to register the multichannel solar images. In the coarse registration step, we used the regular step gradient descent algorithm as an optimizer to maximize the normalized cross correlation metric. The fine registration step uses the Powell–Brent algorithms as an optimizer and brings the Mattes mutual information similarity metric to the minimum. We selected five pairs of images with different resolutions, rotation angles, and shifts to compare and evaluate our results to those obtained by scale-invariant feature transform and phase correlation. The images are observed by the 1.6 m Goode Solar Telescope at Big Bear Solar Observatory and the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Furthermore, we used the mutual information and registration time criteria to quantify the registration results. The results prove that the proposed method not only reaches better registration precision but also has better robustness. Meanwhile, we want to highlight that the method can also work well for the time-series solar image registration.