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Existing learning-based hyperspectral reconstruction methods show limitationsin fully exploiting the information among the hyperspectral bands. As such, wepropose to investigate the chromatic inter-dependencies in their respectivehyperspectral embedding space. These embedded features can be fully exploitedby querying the inter-channel correlations in a combinatorial manner, with theunique and complementary information efficiently fused into the finalprediction. We found such independent modeling and combinatorial excavationmechanisms are extremely beneficial to uncover marginal spectral features,especially in the long wavelength bands. In addition, we have proposed aspatio-spectral attention block and a spectrum-fusion attention module, whichgreatly facilitates the excavation and fusion of information at bothsemantically long-range levels and fine-grained pixel levels across alldimensions. Extensive quantitative and qualitative experiments show that ourmethod (dubbed CESST) achieves SOTA performance. Code for this project is at:https://github.com/AlexYangxx/CESST.

Hyperspectral Image Reconstruction via Combinatorial Embedding of Cross-Channel Spatio-Spectral Clues