# Learning to Deblur and Generate High Frame Rate Video with an Event Camera

arXiv: https://arxiv.org/abs/2003.00847 · HTML: https://mlsanity.com/paper/2003.00847/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2020.

## Built on by (most cited first, 22 of 22)

- [Event-Based Fusion for Motion Deblurring with Cross-modal Attention](https://mlsanity.com/paper/2112.00167/) (2021)
- [EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation](https://mlsanity.com/paper/2111.12341/) (2021)
- [Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image Translation](https://mlsanity.com/paper/2109.01801/) (2021)
- [Mitigating Motion Blur in Neural Radiance Fields with Events and Frames](https://mlsanity.com/paper/2403.19780/) (2024)
- [A Unified Framework for Event-based Frame Interpolation with Ad-hoc Deblurring in the Wild](https://mlsanity.com/paper/2301.05191/) (2023)
- [DiET-GS: Diffusion Prior and Event Stream-Assisted Motion Deblurring 3D Gaussian Splatting](https://mlsanity.com/paper/2503.24210/) (2025)
- [Low-Light Image Enhancement using Event-Based Illumination Estimation](https://mlsanity.com/paper/2504.09379/) (2025)
- [Event-based Image Deblurring with Dynamic Motion Awareness](https://mlsanity.com/paper/2208.11398/) (2022)
- [UniINR: Event-guided Unified Rolling Shutter Correction, Deblurring, and Interpolation](https://mlsanity.com/paper/2305.15078/) (2023)
- [ColibriUAV: An Ultra-Fast, Energy-Efficient Neuromorphic Edge Processing UAV-Platform with Event-Based and Frame-Based Cameras](https://mlsanity.com/paper/2305.18371/) (2023)
- [SiamEvent: Event-based Object Tracking via Edge-aware Similarity Learning with Siamese Networks](https://mlsanity.com/paper/2109.13456/) (2021)
- [Neural Image Re-Exposure](https://mlsanity.com/paper/2305.13593/) (2023)
- [CrossZoom: Simultaneously Motion Deblurring and Event Super-Resolving](https://mlsanity.com/paper/2309.16949/) (2023)
- [EvMAPPER: High Altitude Orthomapping with Event Cameras](https://mlsanity.com/paper/2409.18120/) (2024)
- [Deformable Convolutions and LSTM-based Flexible Event Frame Fusion Network for Motion Deblurring](https://mlsanity.com/paper/2306.00834/) (2023)
- [Deep Hybrid Camera Deblurring for Smartphone Cameras](https://mlsanity.com/paper/2312.13317/) (2023)
- [Neuromorphic Synergy for Video Binarization](https://mlsanity.com/paper/2402.12644/) (2024)
- [HDR Imaging for Dynamic Scenes with Events](https://mlsanity.com/paper/2404.03210/) (2024)
- [EventDiff: A Unified and Efficient Diffusion Model Framework for Event-based Video Frame Interpolation](https://mlsanity.com/paper/2505.08235/) (2025)
- [Structured light with a million light planes per second](https://mlsanity.com/paper/2411.18597/) (2024)
- [EvDiff: Event-Based Video Reconstruction using One-Step Diffusion Models](https://mlsanity.com/paper/2511.17492/) (2025)
- [Weaving Light and Time: Unified Harmonic-Geometric Representation Learning for Dense RGB-Event Parsing](https://mlsanity.com/paper/2607.09143/) (2026)

## Abstract (arXiv)

Event cameras are bio-inspired cameras which can measure the change of intensity asynchronously with high temporal resolution. One of the event cameras' advantages is that they do not suffer from motion blur when recording high-speed scenes. In this paper, we formulate the deblurring task on traditional cameras directed by events to be a residual learning one, and we propose corresponding network architectures for effective learning of deblurring and high frame rate video generation tasks. We first train a modified U-Net network to restore a sharp image from a blurry image using corresponding events. Then we train another similar network with different downsampling blocks to generate high frame rate video using the restored sharp image and events. Experiment results show that our method can restore sharper images and videos than state-of-the-art methods.