Snapshot Compressive Imaging: Principle, Implementation, Theory, Algorithms and Applications

TL;DR

Snapshot compressive imaging (SCI) enables capturing high-dimensional data in a single snapshot using novel optical designs and algorithms, with recent advances driven by deep learning and theoretical guarantees.

Contribution

This paper reviews recent developments in SCI hardware, theory, algorithms, and applications, highlighting the integration of deep learning and recent theoretical insights.

Findings

Deep neural networks improve reconstruction quality in SCI.

Theoretical guarantees for SCI have been recently established.

SCI has diverse applications in hyperspectral imaging, video, and microscopy.

Abstract

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.