Deep plug-and-play priors for spectral snapshot compressive imaging

Siming Zheng

Abstract:

We propose a plug-and-play (PnP) method that uses deep-learning-based denoisers as regularization priors for
spectral snapshot compressive imaging (SCI). Our method is efficient in terms of reconstruction quality and speed
trade-off, and flexible enough to be ready to use for different compressive coding mechanisms. We demonstrate
the efficiency and flexibility in both simulations and five different spectral SCI systems and show that the proposed
deep PnP prior could achieve state-of-the-art results with a simple plug-in based on the optimization framework.
This paves the way for capturing and recovering multi- or hyperspectral information in one snapshot,
which might inspire intriguing applications in remote sensing, biomedical science, and material science. Our
code is available at: https://github.com/zsm1211/PnP-CASSI.