TY - JOUR
T1 - Snapshot Compressive Imaging
T2 - Theory, Algorithms, and Applications
AU - Yuan, Xin
AU - Brady, David J.
AU - Katsaggelos, Aggelos K.
N1 - Publisher Copyright:
© 1991-2012 IEEE.
PY - 2021/3
Y1 - 2021/3
N2 - 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.
AB - 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.
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U2 - 10.1109/MSP.2020.3023869
DO - 10.1109/MSP.2020.3023869
M3 - Article
AN - SCOPUS:85102034111
VL - 38
SP - 65
EP - 88
JO - IEEE Audio and Electroacoustics Newsletter
JF - IEEE Audio and Electroacoustics Newsletter
SN - 1053-5888
IS - 2
M1 - 9363502
ER -