# Dynamic Low-light Imaging with Quanta Image Sensors

arXiv: https://arxiv.org/abs/2007.08614 · HTML: https://mlsanity.com/paper/2007.08614/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2020.

## Built on by (most cited first, 16 of 16)

- [Proto2Proto: Can you recognize the car, the way I do?](https://mlsanity.com/paper/2204.11830/) (2022)
- [Progressive Joint Low-light Enhancement and Noise Removal for Raw Images](https://mlsanity.com/paper/2106.14844/) (2021)
- [Photon-Starved Scene Inference using Single Photon Cameras](https://mlsanity.com/paper/2107.11001/) (2021)
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- [ISP Distillation](https://mlsanity.com/paper/2101.10203/) (2021)
- [Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision](https://mlsanity.com/paper/2109.08880/) (2021)
- [Spatially Varying Exposure with 2-by-2 Multiplexing: Optimality and Universality](https://mlsanity.com/paper/2306.17367/) (2023)
- [Streaming quanta sensors for online, high-performance imaging and vision](https://mlsanity.com/paper/2406.00859/) (2024)
- [Quanta Video Restoration](https://mlsanity.com/paper/2410.14994/) (2024)
- [Student-Teacher Learning from Clean Inputs to Noisy Inputs](https://mlsanity.com/paper/2103.07600/) (2021)
- [An Effective Image Restorer: Denoising and Luminance Adjustment for Low-photon-count Imaging](https://mlsanity.com/paper/2110.15715/) (2021)
- [Generative Quanta Color Imaging](https://mlsanity.com/paper/2403.19066/) (2024)
- [Photon Inhibition for Energy-Efficient Single-Photon Imaging](https://mlsanity.com/paper/2409.18337/) (2024)
- [Personalized Generative Low-light Image Denoising and Enhancement](https://mlsanity.com/paper/2412.14327/) (2024)
- [Joint Depth and Reflectivity Estimation using Single-Photon LiDAR](https://mlsanity.com/paper/2505.13250/) (2025)
- [gQIR: Generative Quanta Image Reconstruction](https://mlsanity.com/paper/2602.20417/) (2026)

## Abstract (arXiv)

Imaging in low light is difficult because the number of photons arriving at the sensor is low. Imaging dynamic scenes in low-light environments is even more difficult because as the scene moves, pixels in adjacent frames need to be aligned before they can be denoised. Conventional CMOS image sensors (CIS) are at a particular disadvantage in dynamic low-light settings because the exposure cannot be too short lest the read noise overwhelms the signal. We propose a solution using Quanta Image Sensors (QIS) and present a new image reconstruction algorithm. QIS are single-photon image sensors with photon counting capabilities. Studies over the past decade have confirmed the effectiveness of QIS for low-light imaging but reconstruction algorithms for dynamic scenes in low light remain an open problem. We fill the gap by proposing a student-teacher training protocol that transfers knowledge from a motion teacher and a denoising teacher to a student network. We show that dynamic scenes can be reconstructed from a burst of frames at a photon level of 1 photon per pixel per frame. Experimental results confirm the advantages of the proposed method compared to existing methods.