用生成模型提升太阳磁图时间分辨率，让动态变化更清晰。
Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative Model
- 用条件扩散模型合成中间帧，实现时间超分辨率。
- 在活跃区动态变化场景下，效果优于线性插值。
- 适合研究太阳磁场快速演化过程的科研人员。
我们提出一种名为 GenMDI 的深度生成模型，用于提升由太阳和日球层观测台（SOHO）上麦克尔逊多普勒成像仪（MDI）获取的太阳活跃区（ARs）视向磁图的时间分辨率。与以往专注于空间超分辨率的研究不同，该方法可实现时间超分辨率，通过在已观测磁图之间生成合成数据，提供更精细的时间结构和更丰富的视向数据细节。GenMDI 模型采用条件扩散过程，综合考虑前后磁图信息，确保生成图像不仅质量高，且与周围数据在时间上保持一致。实验结果表明，该模型在磁场动态演化活跃区的表现优于传统线性插值方法。
原文摘要 · Abstract (English)
We present a novel deep generative model, named GenMDI, to improve the temporal resolution of line-of-sight (LOS) magnetograms of solar active regions (ARs) collected by the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO). Unlike previous studies that focus primarily on spatial super-resolution of MDI magnetograms, our approach can perform temporal super-resolution, which generates and inserts synthetic data between observed MDI magnetograms, thus providing finer temporal structure and enhanced details in the LOS data. The GenMDI model employs a conditional diffusion process, which synthesizes images by considering both preceding and subsequent magnetograms, ensuring that the generated images are not only of high-quality, but also temporally coherent with the surrounding data. Experimental results show that the GenMDI model performs better than the traditional linear interpolation method, especially in ARs with dynamic evolution in magnetic fields.
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