Deep Learning for Compressive Spectral Imaging

We develop a deep learning network to recover hyperspectral images (e.g., with 24 spectral channels) from a single shot measurement. A dual-stage generative model is devised, which can finish the reconstruction task within sub-seconds instead of hours taken by the most recently proposed DeSCI algorithm with a higher quality.