  Existing learning-based hyperspectral reconstruction methods show limitations
in fully exploiting the information among the hyperspectral bands. As such, we
propose to investigate the chromatic inter-dependencies in their respective
hyperspectral embedding space. These embedded features can be fully exploited
by querying the inter-channel correlations in a combinatorial manner, with the
unique and complementary information efficiently fused into the final
prediction. We found such independent modeling and combinatorial excavation
mechanisms are extremely beneficial to uncover marginal spectral features,
especially in the long wavelength bands. In addition, we have proposed a
spatio-spectral attention block and a spectrum-fusion attention module, which
greatly facilitates the excavation and fusion of information at both
semantically long-range levels and fine-grained pixel levels across all
dimensions. Extensive quantitative and qualitative experiments show that our
method (dubbed CESST) achieves SOTA performance. Code for this project is at:
https://github.com/AlexYangxx/CESST.
