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

arXiv 2103.04421 ↗First posted 7 Mar 2021eess.IV cs.CVcited by 0

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Abstract (arXiv)

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 (≥3D) 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 119 references that are in the map. First posted on arXiv 7 Mar 2021.

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29 of 119 references.

References · 29

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17 more
  1. 2020GAP-net for Snapshot Compressive Imagingcited by 54
  2. 2017DiffuserCam: Lensless Single-exposure 3D Imagingcited by 51
  3. 2021Plug-and-Play Algorithms for Video Snapshot Compressive Imagingcited by 42
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  6. 2014Low-Cost Compressive Sensing for Color Video and Depthcited by 21
  7. 2016DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensingcited by 20
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  17. 2020Snapshot Interferometric 3D Imaging by Compressive Sensing and Deep Learningcited by 1

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