Deep plug-and-play priors for spectral snapshot compressive imaging
Siming Zheng, Yang Liu, Ziyi Meng, Mu Qiao, Zhishen Tong, Xiaoyu Yang, Shensheng Han, Xin Yuan
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 .