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

arXiv: https://arxiv.org/abs/2103.04421 · HTML: https://mlsanity.com/paper/2103.04421/

## 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.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017)
- [Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applications](https://mlsanity.com/paper/1605.01710/) (2016)
- [Plug-and-Play Methods Provably Converge with Properly Trained Denoisers](https://mlsanity.com/paper/1905.05406/) (2019)
- [Rank Minimization for Snapshot Compressive Imaging](https://mlsanity.com/paper/1807.07837/) (2018)
- [Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imaging](https://mlsanity.com/paper/2003.13654/) (2020)
- [Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures](https://mlsanity.com/paper/1409.2574/) (2014)
- [From Denoising to Compressed Sensing](https://mlsanity.com/paper/1406.4175/) (2014)
- [Coded aperture compressive temporal imaging](https://mlsanity.com/paper/1302.2575/) (2013)
- [GAP-net for Snapshot Compressive Imaging](https://mlsanity.com/paper/2012.08364/) (2020)
- [DiffuserCam: Lensless Single-exposure 3D Imaging](https://mlsanity.com/paper/1710.02134/) (2017)
- [Plug-and-Play Algorithms for Video Snapshot Compressive Imaging](https://mlsanity.com/paper/2101.04822/) (2021)
- [Memory-Efficient Network for Large-scale Video Compressive Sensing](https://mlsanity.com/paper/2103.03089/) (2021)
- [MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing](https://mlsanity.com/paper/2103.01786/) (2021)
- [Low-Cost Compressive Sensing for Color Video and Depth](https://mlsanity.com/paper/1402.6932/) (2014)
- [DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensing](https://mlsanity.com/paper/1607.03343/) (2016)
- [Compressive Hyperspectral Imaging with Side Information](https://mlsanity.com/paper/1502.06260/) (2015)
- [Deep Fully-Connected Networks for Video Compressive Sensing](https://mlsanity.com/paper/1603.04930/) (2016)
- [A Benchmark for Sparse Coding: When Group Sparsity Meets Rank Minimization](https://mlsanity.com/paper/1709.03979/) (2017)
- [Image Compression Based on Compressive Sensing: End-to-End Comparison with JPEG](https://mlsanity.com/paper/1706.01000/) (2017)
- [Adaptive Temporal Compressive Sensing for Video](https://mlsanity.com/paper/1302.3446/) (2013)
- [Various Total Variation for Snapshot Video Compressive Imaging](https://mlsanity.com/paper/2005.08028/) (2020)
- [Classification and Reconstruction of High-Dimensional Signals from Low-Dimensional Features in the Presence of Side Information](https://mlsanity.com/paper/1412.0614/) (2014)
- [Compressive Holographic Video](https://mlsanity.com/paper/1610.09013/) (2016)
- [Computed stereo lensless X-ray imaging](https://mlsanity.com/paper/1909.04520/) (2019)
- [Snapshot Interferometric 3D Imaging by Compressive Sensing and Deep Learning](https://mlsanity.com/paper/2004.02633/) (2020)

## 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.