太阳磁图的超分辨率重建：基于不确定性估计的自适应分层集成学习
Super-Resolution of Solar Magnetograms via Adaptive Stratified Ensemble Learning with Uncertainty Estimation
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中文总结 AI 辅助
针对太阳磁图超分辨率任务，提出基于图像复杂度分层的自适应专家集成方法，利用不确定性估计和轻量级路由器分配样本，实验证明其性能优于现有方法。
中文摘要 AI 辅助
对太阳光球磁图的单图像超分辨率重建，能够实现对不同天基仪器的观测数据进行一致性分析，并支持对太阳磁场演化的长期研究。我们针对从SOHO/MDI（低分辨率）到SDO/HMI（高分辨率）的视线方向（LOS）磁图超分辨率任务，采用了一种改进的RRDBNet架构，并使用ESRGAN预训练权重进行初始化。通过系统的逐图像诊断分析，我们发现图像复杂度是重建误差的主要预测因子。基于这一发现，我们引入了一个由三个专家网络组成的自适应分层专家集成（SSE），并带有不确定性估计，其中每个专家网络通过加权随机采样策略，分别使用来自三个不同复杂度层级的图像进行训练。在推理阶段，一个基于输入图像统计特征的轻量级路由器将每张测试图像分配给相应的专家网络。我们的实验结果表明，所提出的集成方法具有良好的性能，并且优于密切相关的方法。
英文摘要
Single-image super-resolution of Sun's photospheric magnetograms enables consistent analysis across heterogeneous space-based instruments and supports long-term studies of solar magnetic field evolution. We address the super-resolution task from SOHO/MDI (low-resolution) to SDO/HMI (high-resolution) line-of-sight (LOS) magnetograms using a modified RRDBNet architecture initialized by ESRGAN pretrained weights. Through systematic per-image diagnostic analysis, we identify image complexity as the dominant predictor of reconstruction errors. To exploit this finding, we introduce an adaptive stratified specialist ensemble (SSE) of three specialist networks with uncertainty estimation, where each specialist network is trained by images from three different complexity strata using a weighted random sampling strategy. During inference, a lightweight router based on input image statistics assigns each test image to the appropriate specialist network. Our experimental results demonstrate the good performance of the proposed ensemble and its superiority over closely related methods.
发表机构
- Sam Houston State University（萨姆休斯顿州立大学）
- New Jersey Institute of Technology（新泽西理工学院）
机构由 AI 辅助整理，请以论文原文为准。
