用深度学习提升太阳观测图像分辨率至高精度水平
Improving the Spatial Resolution of GONG Solar Images to GST Quality Using Deep Learning
- 基于GAN的超分辨率方法，融合残差密集块与相对判别器
- 恢复黑子本影细节，清晰呈现丝状结构和纤维特征
- 适合需要高分辨太阳图像的科研人员使用
高分辨率太阳成像对捕捉细尺度动态特征（如暗条和纤维）至关重要，但全球振荡网络组（GONG）全盘Hα图像的空间分辨率受限，难以解析小尺度结构。为此，我们提出一种基于GAN的超分辨率方法，将GONG低分辨率（LR）全盘Hα图像提升至与大熊太阳天文台/古德太阳望远镜（BBSO/GST）相当的高质量水平。采用带有残差-残差密集块的Real-ESRGAN与相对判别器，并精心对齐GONG-GST图像对。模型有效恢复了黑子本影内的精细结构，清晰分辨丝状体与纤维，平均均方误差（MSE）为467.15，均方根误差（RMSE）为21.59，互相关系数（CC）达0.7794。图像对间微小错位限制了定量性能，未来工作计划通过改进配准与扩展数据集进一步提升重建质量。
原文摘要 · Abstract (English)
High-resolution (HR) solar imaging is crucial for capturing fine-scale dynamic features such as filaments and fibrils. However, the spatial resolution of the full-disk H$α$ images is limited and insufficient to resolve these small-scale structures. To address this, we propose a GAN-based superresolution approach to enhance low-resolution (LR) full-disk H$α$ images from the Global Oscillation Network Group (GONG) to a quality comparable with HR observations from the Big Bear Solar Observatory/Goode Solar Telescope (BBSO/GST). We employ Real-ESRGAN with Residual-in-Residual Dense Blocks and a relativistic discriminator. We carefully aligned GONG-GST pairs. The model effectively recovers fine details within sunspot penumbrae and resolves fine details in filaments and fibrils, achieving an average mean squared error (MSE) of 467.15, root mean squared error (RMSE) of 21.59, and cross-correlation (CC) of 0.7794. Slight misalignments between image pairs limit quantitative performance, which we plan to address in future work alongside dataset expansion to further improve reconstruction quality.
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