# Generalizing Event-Based Motion Deblurring in Real-World Scenarios

arXiv: https://arxiv.org/abs/2308.05932 · HTML: https://mlsanity.com/paper/2308.05932/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 39 references that are in the map.
- First posted on arXiv 11 Aug 2023.

## Builds on (most cited first, 28 of 28)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [Learning Continuous Image Representation with Local Implicit Image Function](https://mlsanity.com/paper/2012.09161/) (2020)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [Real-Time Intermediate Flow Estimation for Video Frame Interpolation](https://mlsanity.com/paper/2011.06294/) (2020)
- [Unsupervised Degradation Representation Learning for Blind Super-Resolution](https://mlsanity.com/paper/2104.00416/) (2021)
- [Video to Events: Recycling Video Datasets for Event Cameras](https://mlsanity.com/paper/1912.03095/) (2019)
- [TimeLens: Event-based Video Frame Interpolation](https://mlsanity.com/paper/2106.07286/) (2021)
- [Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera](https://mlsanity.com/paper/1811.10180/) (2018)
- [Investigating Tradeoffs in Real-World Video Super-Resolution](https://mlsanity.com/paper/2111.12704/) (2021)
- [Event-Based Fusion for Motion Deblurring with Cross-modal Attention](https://mlsanity.com/paper/2112.00167/) (2021)
- [Deep Image Deblurring: A Survey](https://mlsanity.com/paper/2201.10700/) (2022)
- [Event-aided Direct Sparse Odometry](https://mlsanity.com/paper/2204.07640/) (2022)
- [Event Enhanced High-Quality Image Recovery](https://mlsanity.com/paper/2007.08336/) (2020)
- [Unifying Motion Deblurring and Frame Interpolation with Events](https://mlsanity.com/paper/2203.12178/) (2022)
- [Learning to Extract a Video Sequence from a Single Motion-Blurred Image](https://mlsanity.com/paper/1804.04065/) (2018)
- [Motion Deblurring with Real Events](https://mlsanity.com/paper/2109.13695/) (2021)
- [Reblur2Deblur: Deblurring Videos via Self-Supervised Learning](https://mlsanity.com/paper/1801.05117/) (2018)
- [Exposure Trajectory Recovery from Motion Blur](https://mlsanity.com/paper/2010.02484/) (2020)
- [Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical Flow](https://mlsanity.com/paper/2112.06242/) (2021)
- [Event-guided Deblurring of Unknown Exposure Time Videos](https://mlsanity.com/paper/2112.06988/) (2021)
- [E-CIR: Event-Enhanced Continuous Intensity Recovery](https://mlsanity.com/paper/2203.01935/) (2022)
- [Are High-Resolution Event Cameras Really Needed?](https://mlsanity.com/paper/2203.14672/) (2022)
- [Event-based Synthetic Aperture Imaging with a Hybrid Network](https://mlsanity.com/paper/2103.02376/) (2021)
- [Learning to Super-Resolve Blurry Images with Events](https://mlsanity.com/paper/2302.13766/) (2023)
- [Learning to See Through with Events](https://mlsanity.com/paper/2212.02219/) (2022)

## Built on by (most cited first, 20 of 20)

- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [Towards Real-world Event-guided Low-light Video Enhancement and Deblurring](https://mlsanity.com/paper/2408.14916/) (2024)
- [Implicit Event-RGBD Neural SLAM](https://mlsanity.com/paper/2311.11013/) (2023)
- [BeNeRF: Neural Radiance Fields from a Single Blurry Image and Event Stream](https://mlsanity.com/paper/2407.02174/) (2024)
- [CMTA: Cross-Modal Temporal Alignment for Event-guided Video Deblurring](https://mlsanity.com/paper/2408.14930/) (2024)
- [SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike Streams](https://mlsanity.com/paper/2403.09486/) (2024)
- [Deep learning in motion deblurring: current status, benchmarks and future prospects](https://mlsanity.com/paper/2401.05055/) (2024)
- [Learning Parallax for Stereo Event-based Motion Deblurring](https://mlsanity.com/paper/2309.09513/) (2023)
- [A Dataset and Model for Realistic License Plate Deblurring](https://mlsanity.com/paper/2404.13677/) (2024)
- [USP-Gaussian: Unifying Spike-based Image Reconstruction, Pose Correction and Gaussian Splatting](https://mlsanity.com/paper/2411.10504/) (2024)
- [Event Camera Guided Visual Media Restoration & 3D Reconstruction: A Survey](https://mlsanity.com/paper/2509.09971/) (2025)
- [Event-based Motion Deblurring via Multi-Temporal Granularity Fusion](https://mlsanity.com/paper/2412.11866/) (2024)
- [ClearSight: Human Vision-Inspired Solutions for Event-Based Motion Deblurring](https://mlsanity.com/paper/2501.15808/) (2025)
- [STD-GS: Exploring Frame-Event Interaction for SpatioTemporal-Disentangled Gaussian Splatting to Reconstruct High-Dynamic Scene](https://mlsanity.com/paper/2506.23157/) (2025)
- [Non-Uniform Exposure Imaging via Neuromorphic Shutter Control](https://mlsanity.com/paper/2404.13972/) (2024)
- [BeSplat: Gaussian Splatting from a Single Blurry Image and Event Stream](https://mlsanity.com/paper/2412.19370/) (2024)
- [From Events to Enhancement: A Survey on Event-Based Imaging Technologies](https://mlsanity.com/paper/2505.05488/) (2025)
- [High-Quality and Efficient Turbulence Mitigation with Events](https://mlsanity.com/paper/2603.20708/) (2026)
- [Dual-Exposure Imaging with Events](https://mlsanity.com/paper/2604.10273/) (2026)
- [Spatio-Temporal Difference Guided Motion Deblurring with the Complementary Vision Sensor](https://mlsanity.com/paper/2604.10554/) (2026)

## Abstract (arXiv)

Event-based motion deblurring has shown promising results by exploiting low-latency events. However, current approaches are limited in their practical usage, as they assume the same spatial resolution of inputs and specific blurriness distributions. This work addresses these limitations and aims to generalize the performance of event-based deblurring in real-world scenarios. We propose a scale-aware network that allows flexible input spatial scales and enables learning from different temporal scales of motion blur. A two-stage self-supervised learning scheme is then developed to fit real-world data distribution. By utilizing the relativity of blurriness, our approach efficiently ensures the restored brightness and structure of latent images and further generalizes deblurring performance to handle varying spatial and temporal scales of motion blur in a self-distillation manner. Our method is extensively evaluated, demonstrating remarkable performance, and we also introduce a real-world dataset consisting of multi-scale blurry frames and events to facilitate research in event-based deblurring.