# Video from Stills: Lensless Imaging with Rolling Shutter

arXiv: https://arxiv.org/abs/1905.13221 · HTML: https://mlsanity.com/paper/1905.13221/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2019.

## Built on by (most cited first, 13 of 13)

- [Untrained networks for compressive lensless photography](https://mlsanity.com/paper/2103.07609/) (2021)
- [Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision](https://mlsanity.com/paper/2109.08880/) (2021)
- [Video Reconstruction from a Single Motion Blurred Image using Learned Dynamic Phase Coding](https://mlsanity.com/paper/2112.14768/) (2021)
- [A Simple Framework for 3D Lensless Imaging with Programmable Masks](https://mlsanity.com/paper/2108.07966/) (2021)
- [Towards Robust and Generalizable Lensless Imaging with Modular Learned Reconstruction](https://mlsanity.com/paper/2502.01102/) (2025)
- [10-mega pixel snapshot compressive imaging with a hybrid coded aperture](https://mlsanity.com/paper/2106.15765/) (2021)
- [Learning to See Inside Opaque Liquid Containers using Speckle Vibrometry](https://mlsanity.com/paper/2507.20757/) (2025)
- [Characterization of point-source transient events with a rolling-shutter compressed sensing system](https://mlsanity.com/paper/2408.16868/) (2024)
- [Let There Be Light: Robust Lensless Imaging Under External Illumination With Deep Learning](https://mlsanity.com/paper/2409.16766/) (2024)
- [A Lensless Polarization Camera](https://mlsanity.com/paper/2603.17156/) (2026)
- [Guided Lensless Polarization Imaging](https://mlsanity.com/paper/2603.27357/) (2026)
- [Color-Encoded Illumination for High-Speed Volumetric Scene Reconstruction](https://mlsanity.com/paper/2604.26920/) (2026)
- [Integrated Forward-Inverse Network for Lensless Image Reconstruction](https://mlsanity.com/paper/2607.04608/) (2026)

## Abstract (arXiv)

Because image sensor chips have a finite bandwidth with which to read out pixels, recording video typically requires a trade-off between frame rate and pixel count. Compressed sensing techniques can circumvent this trade-off by assuming that the image is compressible. Here, we propose using multiplexing optics to spatially compress the scene, enabling information about the whole scene to be sampled from a row of sensor pixels, which can be read off quickly via a rolling shutter CMOS sensor. Conveniently, such multiplexing can be achieved with a simple lensless, diffuser-based imaging system. Using sparse recovery methods, we are able to recover 140 video frames at over 4,500 frames per second, all from a single captured image with a rolling shutter sensor. Our proof-of-concept system uses easily-fabricated diffusers paired with an off-the-shelf sensor. The resulting prototype enables compressive encoding of high frame rate video into a single rolling shutter exposure, and exceeds the sampling-limited performance of an equivalent global shutter system for sufficiently sparse objects.