  Hyperspectral compressive imaging takes advantage of compressive sensing
theory to achieve coded aperture snapshot measurement without temporal
scanning, and the entire three-dimensional spatial-spectral data is captured by
a two-dimensional projection during a single integration period. Its core issue
is how to reconstruct the underlying hyperspectral image using compressive
sensing reconstruction algorithms. Due to the diversity in the spectral
response characteristics and wavelength range of different spectral imaging
devices, previous works are often inadequate to capture complex spectral
variations or lack the adaptive capacity to new hyperspectral imagers. In order
to address these issues, we propose an unsupervised spatial-spectral network to
reconstruct hyperspectral images only from the compressive snapshot
measurement. The proposed network acts as a conditional generative model
conditioned on the snapshot measurement, and it exploits the spatial-spectral
attention module to capture the joint spatial-spectral correlation of
hyperspectral images. The network parameters are optimized to make sure that
the network output can closely match the given snapshot measurement according
to the imaging model, thus the proposed network can adapt to different imaging
settings, which can inherently enhance the applicability of the network.
Extensive experiments upon multiple datasets demonstrate that our network can
achieve better reconstruction results than the state-of-the-art methods.
