# Image Classification in the Dark using Quanta Image Sensors

arXiv: https://arxiv.org/abs/2006.02026 · HTML: https://mlsanity.com/paper/2006.02026/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2020.

## Built on by (most cited first, 15 of 15)

- [Instance Segmentation in the Dark](https://mlsanity.com/paper/2304.14298/) (2023)
- [Proto2Proto: Can you recognize the car, the way I do?](https://mlsanity.com/paper/2204.11830/) (2022)
- [Photon-Starved Scene Inference using Single Photon Cameras](https://mlsanity.com/paper/2107.11001/) (2021)
- [ISP Distillation](https://mlsanity.com/paper/2101.10203/) (2021)
- [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)
- [Robust 3D Object Detection using Probabilistic Point Clouds from Single-Photon LiDARs](https://mlsanity.com/paper/2508.00169/) (2025)
- [Student-Teacher Learning from Clean Inputs to Noisy Inputs](https://mlsanity.com/paper/2103.07600/) (2021)
- [Robust Scene Inference under Noise-Blur Dual Corruptions](https://mlsanity.com/paper/2207.11643/) (2022)
- [bit2bit: 1-bit quanta video reconstruction via self-supervised photon prediction](https://mlsanity.com/paper/2410.23247/) (2024)
- [DPFNet: A Dual-branch Dilated Network with Phase-aware Fourier Convolution for Low-light Image Enhancement](https://mlsanity.com/paper/2209.07937/) (2022)
- [Poisson-Gaussian Holographic Phase Retrieval with Score-based Image Prior](https://mlsanity.com/paper/2305.07712/) (2023)
- [Generative Quanta Color Imaging](https://mlsanity.com/paper/2403.19066/) (2024)
- [Technique Report of CVPR 2024 PBDL Challenges](https://mlsanity.com/paper/2406.10744/) (2024)
- [Accurate Simulation Pipeline for Passive Single-Photon Imaging](https://mlsanity.com/paper/2601.12850/) (2026)

## Abstract (arXiv)

State-of-the-art image classifiers are trained and tested using well-illuminated images. These images are typically captured by CMOS image sensors with at least tens of photons per pixel. However, in dark environments when the photon flux is low, image classification becomes difficult because the measured signal is suppressed by noise. In this paper, we present a new low-light image classification solution using Quanta Image Sensors (QIS). QIS are a new type of image sensors that possess photon counting ability without compromising on pixel size and spatial resolution. Numerous studies over the past decade have demonstrated the feasibility of QIS for low-light imaging, but their usage for image classification has not been studied. This paper fills the gap by presenting a student-teacher learning scheme which allows us to classify the noisy QIS raw data. We show that with student-teacher learning, we are able to achieve image classification at a photon level of one photon per pixel or lower. Experimental results verify the effectiveness of the proposed method compared to existing solutions.