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Data-efficient linear hyperspectral imaging through risk-guided co-design
Computational hyperspectral imaging reconstructs spatial-spectral information from limited optical measurements. Linear reconstructors are efficient and interpretable, but often underperform deep nonlinear models, which typically require substantial training data and computational resources. Here we
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