Adaptive ensemble improves detail in solar magnetic field images
Super-Resolution of Solar Magnetograms via Adaptive Stratified Ensemble Learning with Uncertainty Estimation
Computer Vision and Pattern Recognition
Summary
Images of the Sun's magnetic fields help scientists study solar activity, but different satellites produce pictures of varying detail. The authors created a computer method that turns lower-resolution images into higher-resolution ones similar to images from a better satellite. They found that the difficulty of each image affected how well this worked, so they made multiple specialized models to handle different image complexities. Their approach chooses the best model for each image, resulting in clearer and more accurate solar magnetic maps.
What this means in practice
- •For space weather forecasters: Improve solar magnetic field maps by upscaling older low-resolution satellite images with targeted ensemble methods to enhance prediction accuracy.
- •For satellite imaging teams: Integrate adaptive model selection to enhance resolution of solar magnetograms when combining data from heterogeneous instruments.
Authors
Sina Norouzi Kandalan, Haodi Jiang, Jason T. L. Wang, Qin Li
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.