Snapshot Compressive Imaging: Theory, Algorithms, and Applications

TLDR

High‑dimensional data capture remains a long‑standing challenge, and snapshot compressive imaging (SCI) has been applied to hyperspectral imaging, video, holography, tomography, focal‑depth, polarization imaging, and microscopy, with hardware studied for over a decade but theoretical guarantees only recently derived, while deep‑learning approaches are also emerging. This review surveys recent advances in SCI hardware, theory, and both optimization‑based and deep‑learning algorithms, and discusses diverse applications and future outlook. SCI captures high‑dimensional data with a 2D detector that samples the data compressively via novel optical designs, and reconstruction algorithms—both optimization‑based and deep‑learning—recover the HD data cube.

Abstract

Capturing high-dimensional (HD) data is a long-term challenge in signal processing and related fields. Snapshot compressive imaging (SCI) uses a 2D detector to capture HD (≥3D) data in a snapshot measurement. Via novel optical designs, the 2D detector samples the HD data in a 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, and so on. Although 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 optimizationbased and deep learning-based algorithms. Diverse applications and the outlook for SCI are also discussed.