# Memory-Efficient Network for Large-scale Video Compressive Sensing

arXiv: https://arxiv.org/abs/2103.03089 · HTML: https://mlsanity.com/paper/2103.03089/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 57 references that are in the map.
- First posted on arXiv 4 Mar 2021.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [NICE: Non-linear Independent Components Estimation](https://mlsanity.com/paper/1410.8516/) (2014)
- [The 2017 DAVIS Challenge on Video Object Segmentation](https://mlsanity.com/paper/1704.00675/) (2017)
- [Learning to See in the Dark](https://mlsanity.com/paper/1805.01934/) (2018)
- [The Reversible Residual Network: Backpropagation Without Storing Activations](https://mlsanity.com/paper/1707.04585/) (2017)
- [Rank Minimization for Snapshot Compressive Imaging](https://mlsanity.com/paper/1807.07837/) (2018)
- [Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators](https://mlsanity.com/paper/2006.11469/) (2020)
- [Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imaging](https://mlsanity.com/paper/2003.13654/) (2020)
- [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)
- [Plug-and-Play Algorithms for Video Snapshot Compressive Imaging](https://mlsanity.com/paper/2101.04822/) (2021)
- [Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks](https://mlsanity.com/paper/1803.05215/) (2018)
- [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)
- [Compressive Hyperspectral Imaging with Side Information](https://mlsanity.com/paper/1502.06260/) (2015)
- [Memory-efficient Learning for Large-scale Computational Imaging -- NeurIPS deep inverse workshop](https://mlsanity.com/paper/1912.05098/) (2019)
- [Deep Fully-Connected Networks for Video Compressive Sensing](https://mlsanity.com/paper/1603.04930/) (2016)
- [ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Random Measurements](https://mlsanity.com/paper/1601.06892/) (2016)

## Built on by (most cited first, 30 of 30)

- [Self-supervised Neural Networks for Spectral Snapshot Compressive Imaging](https://mlsanity.com/paper/2108.12654/) (2021)
- [Dense Deep Unfolding Network with 3D-CNN Prior for Snapshot Compressive Imaging](https://mlsanity.com/paper/2109.06548/) (2021)
- [Spatial-Temporal Transformer for Video Snapshot Compressive Imaging](https://mlsanity.com/paper/2209.01578/) (2022)
- [EfficientSCI: Densely Connected Network with Space-time Factorization for Large-scale Video Snapshot Compressive Imaging](https://mlsanity.com/paper/2305.10006/) (2023)
- [Invertible Diffusion Models for Compressed Sensing](https://mlsanity.com/paper/2403.17006/) (2024)
- [Deep Optics for Video Snapshot Compressive Imaging](https://mlsanity.com/paper/2404.05274/) (2024)
- [Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision](https://mlsanity.com/paper/2109.08880/) (2021)
- [Deep Equilibrium Models for Video Snapshot Compressive Imaging](https://mlsanity.com/paper/2201.06931/) (2022)
- [Spectral Compressive Imaging Reconstruction Using Convolution and Contextual Transformer](https://mlsanity.com/paper/2201.05768/) (2022)
- [Hierarchical Separable Video Transformer for Snapshot Compressive Imaging](https://mlsanity.com/paper/2407.11946/) (2024)
- [Two-Stage is Enough: A Concise Deep Unfolding Reconstruction Network for Flexible Video Compressive Sensing](https://mlsanity.com/paper/2201.05810/) (2022)
- [Block Modulating Video Compression: An Ultra Low Complexity Image Compression Encoder for Resource Limited Platforms](https://mlsanity.com/paper/2205.03677/) (2022)
- [SCINeRF: Neural Radiance Fields from a Snapshot Compressive Image](https://mlsanity.com/paper/2403.20018/) (2024)
- [Revisit Dictionary Learning for Video Compressive Sensing under the Plug-and-Play Framework](https://mlsanity.com/paper/2110.04966/) (2021)
- [Unfolding Framework with Prior of Convolution-Transformer Mixture and Uncertainty Estimation for Video Snapshot Compressive Imaging](https://mlsanity.com/paper/2306.11316/) (2023)
- [SnapCap: Efficient Snapshot Compressive Video Captioning](https://mlsanity.com/paper/2401.04903/) (2024)
- [Event-Enhanced Snapshot Compressive Videography at 10K FPS](https://mlsanity.com/paper/2404.07551/) (2024)
- [Reinforcement Learning for Adaptive Video Compressive Sensing](https://mlsanity.com/paper/2105.08205/) (2021)
- [Motion-aware Dynamic Graph Neural Network for Video Compressive Sensing](https://mlsanity.com/paper/2203.00387/) (2022)
- [Untrained Neural Nets for Snapshot Compressive Imaging: Theory and Algorithms](https://mlsanity.com/paper/2406.03694/) (2024)
- [SCIGS: 3D Gaussians Splatting from a Snapshot Compressive Image](https://mlsanity.com/paper/2411.12471/) (2024)
- [Using Powerful Prior Knowledge of Diffusion Model in Deep Unfolding Networks for Image Compressive Sensing](https://mlsanity.com/paper/2503.08429/) (2025)
- [Snapshot Compressive Imaging: Principle, Implementation, Theory, Algorithms and Applications](https://mlsanity.com/paper/2103.04421/) (2021)
- [Sampling-Priors-Augmented Deep Unfolding Network for Robust Video Compressive Sensing](https://mlsanity.com/paper/2307.07291/) (2023)
- [Ultra-Low Complexity On-Orbit Compression for Remote Sensing Imagery via Block Modulated Imaging](https://mlsanity.com/paper/2412.18417/) (2024)
- [Vision without Images: End-to-End Computer Vision from Single Compressive Measurements](https://mlsanity.com/paper/2501.15122/) (2025)
- [Sparse Transformer for Ultra-sparse Sampled Video Compressive Sensing](https://mlsanity.com/paper/2509.08228/) (2025)
- [3One2: One-step Regression Plus One-step Diffusion for One-hot Modulation in Dual-path Video Snapshot Compressive Imaging](https://mlsanity.com/paper/2512.17578/) (2025)
- [RobustSCI: Beyond Reconstruction to Restoration for Snapshot Compressive Imaging under Real-World Degradations](https://mlsanity.com/paper/2603.07489/) (2026)
- [GS2CI: Robust Gaussian Splatting For Snapshot Compressive Imaging via Large Vision Model Priors](https://mlsanity.com/paper/2608.13502/) (2026)

## Abstract (arXiv)

Video snapshot compressive imaging (SCI) captures a sequence of video frames in a single shot using a 2D detector. The underlying principle is that during one exposure time, different masks are imposed on the high-speed scene to form a compressed measurement. With the knowledge of masks, optimization algorithms or deep learning methods are employed to reconstruct the desired high-speed video frames from this snapshot measurement. Unfortunately, though these methods can achieve decent results, the long running time of optimization algorithms or huge training memory occupation of deep networks still preclude them in practical applications. In this paper, we develop a memory-efficient network for large-scale video SCI based on multi-group reversible 3D convolutional neural networks. In addition to the basic model for the grayscale SCI system, we take one step further to combine demosaicing and SCI reconstruction to directly recover color video from Bayer measurements. Extensive results on both simulation and real data captured by SCI cameras demonstrate that our proposed model outperforms previous state-of-the-art with less memory and thus can be used in large-scale problems. The code is at https://github.com/BoChenGroup/RevSCI-net.