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.

中文翻译：

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深度即插即用的先验技术，用于频谱快照压缩成像

我们提出了一种即插即用（PnP）方法，该方法使用基于深度学习的降噪器作为频谱快照压缩成像（SCI）的正则化先验。我们的方法在重建质量和速度折衷方面是高效的，并且足够灵活以准备用于不同的压缩编码机制。我们在仿真和五种不同的光谱SCI系统中均展示了效率和灵活性，并表明，提出的深层PnP先验技术可以通过基于优化框架的简单插件来获得最新结果。这为在一个快照中捕获和恢复多光谱或高光谱信息铺平了道路，这可能会激发在遥感，生物医学和材料科学领域的有趣应用。我们的代码位于：https://github.com/zsm1211/PnP-CASSI。