# Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approach

arXiv: https://arxiv.org/abs/2404.00834 · HTML: https://mlsanity.com/paper/2404.00834/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 61 references that are in the map.
- First posted on arXiv 1 Apr 2024.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Mixed Precision Training](https://mlsanity.com/paper/1710.03740/) (2017)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [Learning to See in the Dark](https://mlsanity.com/paper/1805.01934/) (2018)
- [Kindling the Darkness: A Practical Low-light Image Enhancer](https://mlsanity.com/paper/1905.04161/) (2019)
- [The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception](https://mlsanity.com/paper/1801.10202/) (2018)
- [Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement](https://mlsanity.com/paper/2303.06705/) (2023)
- [Events-to-Video: Bringing Modern Computer Vision to Event Cameras](https://mlsanity.com/paper/1904.08298/) (2019)
- [DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks](https://mlsanity.com/paper/1704.02470/) (2017)
- [Low-Light Image and Video Enhancement Using Deep Learning: A Survey](https://mlsanity.com/paper/2104.10729/) (2021)
- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [Low-Light Image Enhancement via Structure Modeling and Guidance](https://mlsanity.com/paper/2305.05839/) (2023)
- [Event Enhanced High-Quality Image Recovery](https://mlsanity.com/paper/2007.08336/) (2020)
- [Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement](https://mlsanity.com/paper/2304.07039/) (2023)
- [STAR: A Structure and Texture Aware Retinex Model](https://mlsanity.com/paper/1906.06690/) (2019)
- [CED: Color Event Camera Dataset](https://mlsanity.com/paper/1904.10772/) (2019)
- [Low-Light Video Enhancement with Synthetic Event Guidance](https://mlsanity.com/paper/2208.11014/) (2022)
- [Low-Light Image Enhancement with Illumination-Aware Gamma Correction and Complete Image Modelling Network](https://mlsanity.com/paper/2308.08220/) (2023)
- [Human Pose Estimation in Extremely Low-Light Conditions](https://mlsanity.com/paper/2303.15410/) (2023)
- [Towards Dynamic and Small Objects Refinement for Unsupervised Domain Adaptative Nighttime Semantic Segmentation](https://mlsanity.com/paper/2310.04747/) (2023)

## Built on by (most cited first, 30 of 48)

- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [EvLight++: Low-Light Video Enhancement with an Event Camera: A Large-Scale Real-World Dataset, Novel Method, and More](https://mlsanity.com/paper/2408.16254/) (2024)
- [Low-Light Image Enhancement using Event-Based Illumination Estimation](https://mlsanity.com/paper/2504.09379/) (2025)
- [From Sim-to-Real: Toward General Event-based Low-light Frame Interpolation with Per-scene Optimization](https://mlsanity.com/paper/2406.08090/) (2024)
- [Helios: An extremely low power event-based gesture recognition for always-on smart eyewear](https://mlsanity.com/paper/2407.05206/) (2024)
- [CLIP-Guided Data Augmentation for Night-Time Image Dehazing](https://mlsanity.com/paper/2604.05500/) (2026)
- [Event Camera Meets Mobile Embodied Perception: Abstraction, Algorithm, Acceleration, Application](https://mlsanity.com/paper/2503.22943/) (2025)
- [MouseSIS: A Frames-and-Events Dataset for Space-Time Instance Segmentation of Mice](https://mlsanity.com/paper/2409.03358/) (2024)
- [HUE Dataset: High-Resolution Event and Frame Sequences for Low-Light Vision](https://mlsanity.com/paper/2410.19164/) (2024)
- [SEE: See Everything Every Time -- Adaptive Brightness Adjustment for Broad Light Range Images via Events](https://mlsanity.com/paper/2502.21120/) (2025)
- [Dark-EvGS: Event Camera as an Eye for Radiance Field in the Dark](https://mlsanity.com/paper/2507.11931/) (2025)
- [EventVGGT: Exploring Cross-Modal Distillation for Consistent Event-based Depth Estimation](https://mlsanity.com/paper/2603.09385/) (2026)
- [E-VLA: Event-Augmented Vision-Language-Action Model for Dark and Blurred Scenes](https://mlsanity.com/paper/2604.04834/) (2026)
- [Event Cameras Meet SPADs for High-Speed, Low-Bandwidth Imaging](https://mlsanity.com/paper/2404.11511/) (2024)
- [From Sharp to Blur: Unsupervised Domain Adaptation for 2D Human Pose Estimation Under Extreme Motion Blur Using Event Cameras](https://mlsanity.com/paper/2507.22438/) (2025)
- [Exploring Fourier Prior and Event Collaboration for Low-Light Image Enhancement](https://mlsanity.com/paper/2508.00308/) (2025)
- [Event Camera Guided Visual Media Restoration & 3D Reconstruction: A Survey](https://mlsanity.com/paper/2509.09971/) (2025)
- [EAG3R: Event-Augmented 3D Geometry Estimation for Dynamic and Extreme-Lighting Scenes](https://mlsanity.com/paper/2512.00771/) (2025)
- [EvRWKV: A Continuous Interactive RWKV Framework for Effective Event-Guided Low-Light Image Enhancement](https://mlsanity.com/paper/2507.03184/) (2025)
- [Seeing the Unseen: Zooming in the Dark with Event Cameras](https://mlsanity.com/paper/2601.02206/) (2026)
- [EventGait: Towards Robust Gait Recognition with Event Streams](https://mlsanity.com/paper/2605.22139/) (2026)
- [Event-Illumination Collaborative Low-light Image Enhancement with a High-resolution Real-world Dataset](https://mlsanity.com/paper/2605.22186/) (2026)
- [ExFMan: Rendering 3D Dynamic Humans with Hybrid Monocular Blurry Frames and Events](https://mlsanity.com/paper/2409.14103/) (2024)
- [AsynEIO: Asynchronous Monocular Event-Inertial Odometry Using Gaussian Process Regression](https://mlsanity.com/paper/2411.12175/) (2024)
- [Helios 2.0: A Robust, Ultra-Low Power Gesture Recognition System Optimised for Event-Sensor based Wearables](https://mlsanity.com/paper/2503.07825/) (2025)
- [LENVIZ: A High-Resolution Low-Exposure Night Vision Benchmark Dataset](https://mlsanity.com/paper/2503.19804/) (2025)
- [Bidirectional Image-Event Guided Fusion Framework for Low-Light Image Enhancement](https://mlsanity.com/paper/2506.06120/) (2025)
- [Benchmarking Microsaccade Recognition with Event Cameras: A Novel Dataset and Evaluation](https://mlsanity.com/paper/2510.24231/) (2025)
- [BiEvLight: Bi-level Learning of Task-Aware Event Refinement for Low-Light Image Enhancement](https://mlsanity.com/paper/2603.04975/) (2026)
- [x2-Fusion: Cross-Modality and Cross-Dimension Flow Estimation in Event Edge Space](https://mlsanity.com/paper/2603.16671/) (2026)

## Abstract (arXiv)

Event camera has recently received much attention for low-light image enhancement (LIE) thanks to their distinct advantages, such as high dynamic range. However, current research is prohibitively restricted by the lack of large-scale, real-world, and spatial-temporally aligned event-image datasets. To this end, we propose a real-world (indoor and outdoor) dataset comprising over 30K pairs of images and events under both low and normal illumination conditions. To achieve this, we utilize a robotic arm that traces a consistent non-linear trajectory to curate the dataset with spatial alignment precision under 0.03mm. We then introduce a matching alignment strategy, rendering 90% of our dataset with errors less than 0.01s. Based on the dataset, we propose a novel event-guided LIE approach, called EvLight, towards robust performance in real-world low-light scenes. Specifically, we first design the multi-scale holistic fusion branch to extract holistic structural and textural information from both events and images. To ensure robustness against variations in the regional illumination and noise, we then introduce a Signal-to-Noise-Ratio (SNR)-guided regional feature selection to selectively fuse features of images from regions with high SNR and enhance those with low SNR by extracting regional structure information from events. Extensive experiments on our dataset and the synthetic SDSD dataset demonstrate our EvLight significantly surpasses the frame-based methods. Code and datasets are available at https://vlislab22.github.io/eg-lowlight/.