AI RESEARCH
Radiative-Structured Neural Operator for Continuous and Extrapolative Spectral Super-Resolution
arXiv CS.CV
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ArXi:2511.17895v2 Announce Type: replace-cross Spectral super-resolution (SSR) aims to reconstruct hyperspectral images (HSIs) from multispectral observations, with broad applications in computer vision and remote sensing. Deep learning-based methods have been widely used, but they often treat spectra as discrete vectors learned from data, rather than continuous curves constrained by physics principles, leading to unrealistic predictions and limited applicability.