Snapshot Compressive Imaging: Principle, Implementation, Theory, Algorithms and Applications
It addresses the challenge of capturing high-dimensional data in signal processing, but as a review article, it is incremental in summarizing existing advances rather than presenting new research.
The paper reviews snapshot compressive imaging (SCI), which captures high-dimensional data using a 2D detector in a single snapshot, and discusses its hardware, theory, algorithms, and applications across fields like hyperspectral imaging and video.
Capturing high-dimensional (HD) data is a long-term challenge in signal processing and related fields. Snapshot compressive imaging (SCI) uses a two-dimensional (2D) detector to capture HD ($\ge3$D) data in a {\em snapshot} measurement. Via novel optical designs, the 2D detector samples the HD data in a {\em compressive} manner; following this, algorithms are employed to reconstruct the desired HD data-cube. SCI has been used in hyperspectral imaging, video, holography, tomography, focal depth imaging, polarization imaging, microscopy, \etc.~Though the hardware has been investigated for more than a decade, the theoretical guarantees have only recently been derived. Inspired by deep learning, various deep neural networks have also been developed to reconstruct the HD data-cube in spectral SCI and video SCI. This article reviews recent advances in SCI hardware, theory and algorithms, including both optimization-based and deep-learning-based algorithms. Diverse applications and the outlook of SCI are also discussed.