# DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensing

arXiv: https://arxiv.org/abs/1607.03343 · HTML: https://mlsanity.com/paper/1607.03343/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2016.

## Built on by (most cited first, 20 of 20)

- [MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing](https://mlsanity.com/paper/2103.01786/) (2021)
- [Learning Spatially Varying Pixel Exposures for Motion Deblurring](https://mlsanity.com/paper/2204.07267/) (2022)
- [Time-Efficient Light-Field Acquisition Using Coded Aperture and Events](https://mlsanity.com/paper/2403.07244/) (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 Optical Coding Design in Computational Imaging](https://mlsanity.com/paper/2207.00164/) (2022)
- [Acquiring a Dynamic Light Field through a Single-Shot Coded Image](https://mlsanity.com/paper/2204.12089/) (2022)
- [Unfolding Framework with Prior of Convolution-Transformer Mixture and Uncertainty Estimation for Video Snapshot Compressive Imaging](https://mlsanity.com/paper/2306.11316/) (2023)
- [Reinforcement Learning for Adaptive Video Compressive Sensing](https://mlsanity.com/paper/2105.08205/) (2021)
- [Untrained Neural Nets for Snapshot Compressive Imaging: Theory and Algorithms](https://mlsanity.com/paper/2406.03694/) (2024)
- [Integration of Programmable Diffraction with Digital Neural Networks](https://mlsanity.com/paper/2406.10688/) (2024)
- [Adversarial Attacks in Weight-Space Classifiers](https://mlsanity.com/paper/2502.20314/) (2025)
- [Collaborative On-Sensor Array Cameras](https://mlsanity.com/paper/2506.04061/) (2025)
- [Coded-E2LF: Coded Aperture Light Field Imaging from Events](https://mlsanity.com/paper/2602.22620/) (2026)
- [Scalable Deep Compressive Sensing](https://mlsanity.com/paper/2101.08024/) (2021)
- [Snapshot Compressive Imaging: Principle, Implementation, Theory, Algorithms and Applications](https://mlsanity.com/paper/2103.04421/) (2021)
- [Deep Learning Techniques for Compressive Sensing-Based Reconstruction and Inference -- A Ubiquitous Systems Perspective](https://mlsanity.com/paper/2105.13191/) (2021)
- [Unsupervised Learning for Target Tracking and Background Subtraction in Satellite Imagery](https://mlsanity.com/paper/2109.00885/) (2021)
- [Human-Imperceptible Identification with Learnable Lensless Imaging](https://mlsanity.com/paper/2302.02255/) (2023)
- [Vision without Images: End-to-End Computer Vision from Single Compressive Measurements](https://mlsanity.com/paper/2501.15122/) (2025)

## Abstract (arXiv)

In this paper, we propose a novel encoder-decoder neural network model referred to as DeepBinaryMask for video compressive sensing. In video compressive sensing one frame is acquired using a set of coded masks (sensing matrix) from which a number of video frames is reconstructed, equal to the number of coded masks. The proposed framework is an end-to-end model where the sensing matrix is trained along with the video reconstruction. The encoder learns the binary elements of the sensing matrix and the decoder is trained to recover the unknown video sequence. The reconstruction performance is found to improve when using the trained sensing mask from the network as compared to other mask designs such as random, across a wide variety of compressive sensing reconstruction algorithms. Finally, our analysis and discussion offers insights into understanding the characteristics of the trained mask designs that lead to the improved reconstruction quality.