# LED: A Large-scale Real-world Paired Dataset for Event Camera Denoising

arXiv: https://arxiv.org/abs/2405.19718 · HTML: https://mlsanity.com/paper/2405.19718/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 52 references that are in the map.
- First posted on arXiv 30 May 2024.

## Builds on (most cited first, 24 of 24)

- [Noise2Noise: Learning Image Restoration without Clean Data](https://mlsanity.com/paper/1803.04189/) (2018)
- [Noise2Void - Learning Denoising from Single Noisy Images](https://mlsanity.com/paper/1811.10980/) (2018)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks](https://mlsanity.com/paper/2007.05785/) (2020)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [Learning to Detect Objects with a 1 Megapixel Event Camera](https://mlsanity.com/paper/2009.13436/) (2020)
- [A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation](https://mlsanity.com/paper/1804.01306/) (2018)
- [Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images](https://mlsanity.com/paper/2101.02824/) (2021)
- [SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence](https://mlsanity.com/paper/2310.16620/) (2023)
- [Video to Events: Recycling Video Datasets for Event Cameras](https://mlsanity.com/paper/1912.03095/) (2019)
- [Burst Denoising with Kernel Prediction Networks](https://mlsanity.com/paper/1712.02327/) (2017)
- [A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising](https://mlsanity.com/paper/2003.12751/) (2020)
- [Temporal-wise Attention Spiking Neural Networks for Event Streams Classification](https://mlsanity.com/paper/2107.11711/) (2021)
- [Event-Based Motion Segmentation by Motion Compensation](https://mlsanity.com/paper/1904.01293/) (2019)
- [Event-based Video Reconstruction via Potential-assisted Spiking Neural Network](https://mlsanity.com/paper/2201.10943/) (2022)
- [Handheld Mobile Photography in Very Low Light](https://mlsanity.com/paper/1910.11336/) (2019)
- [Event Probability Mask (EPM) and Event Denoising Convolutional Neural Network (EDnCNN) for Neuromorphic Cameras](https://mlsanity.com/paper/2003.08282/) (2020)
- [E-MLB: Multilevel Benchmark for Event-Based Camera Denoising](https://mlsanity.com/paper/2303.11997/) (2023)
- [End-to-end Learning of Object Motion Estimation from Retinal Events for Event-based Object Tracking](https://mlsanity.com/paper/2002.05911/) (2020)
- [Biologically Inspired Dynamic Thresholds for Spiking Neural Networks](https://mlsanity.com/paper/2206.04426/) (2022)
- [Event-based Synthetic Aperture Imaging with a Hybrid Network](https://mlsanity.com/paper/2103.02376/) (2021)
- [Noise2NoiseFlow: Realistic Camera Noise Modeling without Clean Images](https://mlsanity.com/paper/2206.01103/) (2022)
- [Neuromorphic Camera Denoising using Graph Neural Network-driven Transformers](https://mlsanity.com/paper/2112.09685/) (2021)
- [ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation](https://mlsanity.com/paper/2203.11732/) (2022)

## Built on by (most cited first, 12 of 12)

- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [Event Camera Meets Mobile Embodied Perception: Abstraction, Algorithm, Acceleration, Application](https://mlsanity.com/paper/2503.22943/) (2025)
- [EGTM: Event-guided Efficient Turbulence Mitigation](https://mlsanity.com/paper/2509.03808/) (2025)
- [Event Camera Guided Visual Media Restoration & 3D Reconstruction: A Survey](https://mlsanity.com/paper/2509.09971/) (2025)
- [EventTracer: Fast Path Tracing-based Event Stream Rendering](https://mlsanity.com/paper/2508.18071/) (2025)
- [Event-Illumination Collaborative Low-light Image Enhancement with a High-resolution Real-world Dataset](https://mlsanity.com/paper/2605.22186/) (2026)
- [Near-infrared Image Deblurring and Event Denoising with Synergistic Neuromorphic Imaging](https://mlsanity.com/paper/2503.01193/) (2025)
- [Learning from Noise: Enhancing DNNs for Event-Based Vision through Controlled Noise Injection](https://mlsanity.com/paper/2506.03918/) (2025)
- [A Label-Free and Non-Monotonic Metric for Evaluating Denoising in Event Cameras](https://mlsanity.com/paper/2406.08909/) (2024)
- [SPACT18: Spiking Human Action Recognition Benchmark Dataset with Complementary RGB and Thermal Modalities](https://mlsanity.com/paper/2507.16151/) (2025)
- [PSTTS: A Plug-and-Play Token Selector for Efficient Event-based Spatio-temporal Representation Learning](https://mlsanity.com/paper/2509.22481/) (2025)
- [High-Quality and Efficient Turbulence Mitigation with Events](https://mlsanity.com/paper/2603.20708/) (2026)

## Abstract (arXiv)

Event camera has significant advantages in capturing dynamic scene information while being prone to noise interference, particularly in challenging conditions like low threshold and low illumination. However, most existing research focuses on gentle situations, hindering event camera applications in realistic complex scenarios. To tackle this limitation and advance the field, we construct a new paired real-world event denoising dataset (LED), including 3K sequences with 18K seconds of high-resolution (1200*680) event streams and showing three notable distinctions compared to others: diverse noise levels and scenes, larger-scale with high-resolution, and high-quality GT. Specifically, it contains stepped parameters and varying illumination with diverse scenarios. Moreover, based on the property of noise events inconsistency and signal events consistency, we propose a novel effective denoising framework(DED) using homogeneous dual events to generate the GT with better separating noise from the raw. Furthermore, we design a bio-inspired baseline leveraging Leaky-Integrate-and-Fire (LIF) neurons with dynamic thresholds to realize accurate denoising. The experimental results demonstrate that the remarkable performance of the proposed approach on different datasets.The dataset and code are at https://github.com/Yee-Sing/led.