FMG-Pan is a model-guided instance-wise adaptation framework for real-world pansharpening that adds physical fidelity constraints to deliver state-of-the-art fusion quality with training and inference completed in seconds on single image pairs.
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2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
EDNO redefines pansharpening as a frequency-domain functional mapping that decouples fusion via Euler-inspired polar coordinates into explicit phase-rotation simulation and implicit spectral modeling for improved efficiency.
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Fast Model-guided Instance-wise Adaptation Framework for Real-world Pansharpening with Fidelity Constraints
FMG-Pan is a model-guided instance-wise adaptation framework for real-world pansharpening that adds physical fidelity constraints to deliver state-of-the-art fusion quality with training and inference completed in seconds on single image pairs.
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Euler-inspired Decoupling Neural Operator for Efficient Pansharpening
EDNO redefines pansharpening as a frequency-domain functional mapping that decouples fusion via Euler-inspired polar coordinates into explicit phase-rotation simulation and implicit spectral modeling for improved efficiency.