{"slug": "super-resolution-of-solar-magnetograms-via-adaptive-stratified-ensemble-learning", "title": "Super-Resolution of Solar Magnetograms via Adaptive Stratified Ensemble Learning with Uncertainty Estimation", "summary": "A team of researchers developed an adaptive stratified specialist ensemble (SSE) of three specialist networks with uncertainty estimation to super-resolve SOHO/MDI low-resolution line-of-sight magnetograms into SDO/HMI high-resolution images, according to an arXiv paper (arXiv:2609.27131v1). The work modifies the RRDBNet architecture initialized with ESRGAN pretrained weights and identifies image complexity as the dominant predictor of reconstruction errors, training each specialist on one of three complexity strata via weighted random sampling. At inference, a lightweight router based on input image statistics assigns each test image to the appropriate specialist network, and the authors report the ensemble outperforms closely related methods.", "body_md": "arXiv:2609.27131v1 Announce Type: new \nAbstract: 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.", "url": "https://wpnews.pro/news/super-resolution-of-solar-magnetograms-via-adaptive-stratified-ensemble-learning", "canonical_source": "https://arxiv.org/abs/2609.27131", "published_at": "2026-09-24 04:00:00+00:00", "updated_at": "2026-09-24 04:01:48.625733+00:00", "lang": "en", "topics": ["machine-learning", "computer-vision", "ai-research", "neural-networks"], "entities": ["SOHO/MDI", "SDO/HMI", "RRDBNet", "ESRGAN", "arXiv"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/super-resolution-of-solar-magnetograms-via-adaptive-stratified-ensemble-learning", "markdown": "https://wpnews.pro/news/super-resolution-of-solar-magnetograms-via-adaptive-stratified-ensemble-learning.md", "text": "https://wpnews.pro/news/super-resolution-of-solar-magnetograms-via-adaptive-stratified-ensemble-learning.txt", "jsonld": "https://wpnews.pro/news/super-resolution-of-solar-magnetograms-via-adaptive-stratified-ensemble-learning.jsonld"}}