Problem
Research questions and friction points this paper is trying to address.
super-resolution
solar magnetograms
heterogeneous space-based instruments
long-term studies
solar magnetic field evolution
Super-Resolution of Solar Magnetograms via Adaptive Stratified Ensemble Learning with Uncertainty Estimation
๐ค AI Summary
ๆฌๆ้่ฟ่ช้ๅบๅๅฑไธๅฎถ้ๆๅญฆไน ๆนๆณ่งฃๅณไบๅคช้ณ็ฃๅพ็่ถ
ๅ่พจ็้ฎ้ข๏ผๆ้ซไบไปไฝๅ่พจ็ๅฐ้ซๅ่พจ็็ฃๅพ้ๅปบ็ๅ็กฎๆงใ
๐ Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Adaptive Stratified Ensemble Learning
Uncertainty Estimation
Image Complexity
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