High Resolution Solar Image Generation Using Generative Adversarial Networks

Q1 Decision Sciences Annals of Data Science Pub Date : 2022-08-02 DOI:10.1007/s40745-022-00436-2
Ankan Dash, Junyi Ye, Guiling Wang, Huiran Jin
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引用次数: 0

Abstract

We applied Deep Learning algorithm known as Generative Adversarial Networks (GANs) to perform solar image-to-image translation. That is, from Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) line of sight magnetogram images to SDO/Atmospheric Imaging Assembly (AIA) 0304-Å images. The Ultraviolet (UV)/Extreme Ultraviolet observations like the SDO/AIA 0304-Å images were only made available to scientists in the late 1990s even though the magnetic field observations like the SDO/HMI have been available since the 1970s. Therefore, by leveraging Deep Learning algorithms like GANs we can give scientists access to complete datasets for analysis. For generating high resolution solar images, we use the Pix2PixHD and Pix2Pix algorithms. The Pix2PixHD algorithm was specifically designed for high resolution image generation tasks, and the Pix2Pix algorithm is by far the most widely used image to image translation algorithm. For training and testing we used the data for the year 2012, 2013 and 2014. After model training, we evaluated the model on the test data. The results show that our deep learning models are capable of generating high resolution (1024 × 1024 pixels) SDO/AIA0304 images from SDO/HMI line of sight magnetograms. Specifically, the pixel-to-pixel Pearson Correlation Coefficient of the images generated by Pix2PixHD and original images is as high as 0.99. The number is 0.962 if Pix2Pix is used to generate images. The results we get for our Pix2PixHD model is better than the results obtained by previous works done by others to generate SDO/AIA 0304 images. Thus, we can use these models to generate AIA0304 images when the AIA0304 data is not available which can be used for understanding space weather and giving researchers the capability to predict solar events such as Solar Flares and Coronal Mass Ejections. As far as we know, our work is the first attempt to leverage Pix2PixHD algorithm for SDO/HMI to SDO/AIA0304 image-to-image translation.

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利用生成式对抗网络生成高分辨率太阳能图像
我们应用称为生成对抗网络(GANs)的深度学习算法来执行太阳图像到图像的转换。也就是说,从太阳动力学天文台(SDO)/地震和磁成像仪(HMI)视线磁图图像到 SDO/Atmospheric Imaging Assembly(AIA)0304-Å 图像。像 SDO/AIA 0304-Å 图像这样的紫外线(UV)/极紫外线观测数据直到 20 世纪 90 年代末才提供给科学家,尽管像 SDO/HMI 这样的磁场观测数据早在 20 世纪 70 年代就已提供。因此,通过利用深度学习算法(如 GAN),我们可以让科学家们获得完整的数据集进行分析。为了生成高分辨率的太阳图像,我们使用了 Pix2PixHD 和 Pix2Pix 算法。Pix2PixHD 算法专为高分辨率图像生成任务而设计,而 Pix2Pix 算法是迄今为止使用最广泛的图像到图像转换算法。我们使用 2012 年、2013 年和 2014 年的数据进行训练和测试。模型训练完成后,我们在测试数据上对模型进行了评估。结果表明,我们的深度学习模型能够从SDO/HMI视线磁图生成高分辨率(1024 × 1024像素)的SDO/AIA0304图像。具体来说,Pix2PixHD 生成的图像与原始图像的像素间皮尔逊相关系数高达 0.99。如果使用 Pix2Pix 生成图像,则相关系数为 0.962。我们的 Pix2PixHD 模型所得到的结果要好于之前其他人在生成 SDO/AIA 0304 图像时所得到的结果。因此,当没有 AIA0304 数据时,我们可以使用这些模型生成 AIA0304 图像,这可用于了解空间天气,并使研究人员有能力预测太阳耀斑和日冕物质抛射等太阳活动。据我们所知,我们的工作是利用 Pix2PixHD 算法进行 SDO/HMI 到 SDO/AIA0304 图像到图像转换的首次尝试。
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来源期刊
Annals of Data Science
Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
6.50
自引率
0.00%
发文量
93
期刊介绍: Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.     ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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