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

Document Type

Article

Publication Date

2-1-2021

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. © 2021 Chinese Laser Press

Identifier

85101196152 (Scopus)

Publication Title

Photonics Research

External Full Text Location

https://doi.org/10.1364/PRJ.411745

ISSN

23279125

First Page

B18

Last Page

B29

Issue

2

Volume

9

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