# Event Enhanced High-Quality Image Recovery

arXiv: https://arxiv.org/abs/2007.08336 · HTML: https://mlsanity.com/paper/2007.08336/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2020.

## Built on by (most cited first, 30 of 68)

- [TimeLens: Event-based Video Frame Interpolation](https://mlsanity.com/paper/2106.07286/) (2021)
- [N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras](https://mlsanity.com/paper/2112.01041/) (2021)
- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [Ev-NeRF: Event Based Neural Radiance Field](https://mlsanity.com/paper/2206.12455/) (2022)
- [Unifying Motion Deblurring and Frame Interpolation with Events](https://mlsanity.com/paper/2203.12178/) (2022)
- [Motion Deblurring with Real Events](https://mlsanity.com/paper/2109.13695/) (2021)
- [Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approach](https://mlsanity.com/paper/2404.00834/) (2024)
- [EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation](https://mlsanity.com/paper/2111.12341/) (2021)
- [Deep Learning for HDR Imaging: State-of-the-Art and Future Trends](https://mlsanity.com/paper/2110.10394/) (2021)
- [Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution](https://mlsanity.com/paper/2303.13767/) (2023)
- [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)
- [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)
- [Towards Real-world Event-guided Low-light Video Enhancement and Deblurring](https://mlsanity.com/paper/2408.14916/) (2024)
- [Generalizing Event-Based Motion Deblurring in Real-World Scenarios](https://mlsanity.com/paper/2308.05932/) (2023)
- [EvGGS: A Collaborative Learning Framework for Event-based Generalizable Gaussian Splatting](https://mlsanity.com/paper/2405.14959/) (2024)
- [EventAid: Benchmarking Event-aided Image/Video Enhancement Algorithms with Real-captured Hybrid Dataset](https://mlsanity.com/paper/2312.08220/) (2023)
- [Video Interpolation by Event-driven Anisotropic Adjustment of Optical Flow](https://mlsanity.com/paper/2208.09127/) (2022)
- [Event-based Synthetic Aperture Imaging with a Hybrid Network](https://mlsanity.com/paper/2103.02376/) (2021)
- [EventHDR: from Event to High-Speed HDR Videos and Beyond](https://mlsanity.com/paper/2409.17029/) (2024)
- [BeNeRF: Neural Radiance Fields from a Single Blurry Image and Event Stream](https://mlsanity.com/paper/2407.02174/) (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)
- [DiET-GS: Diffusion Prior and Event Stream-Assisted Motion Deblurring 3D Gaussian Splatting](https://mlsanity.com/paper/2503.24210/) (2025)
- [Event-guided Multi-patch Network with Self-supervision for Non-uniform Motion Deblurring](https://mlsanity.com/paper/2302.07689/) (2023)
- [Event-based Image Deblurring with Dynamic Motion Awareness](https://mlsanity.com/paper/2208.11398/) (2022)
- [Learning to Super-Resolve Blurry Images with Events](https://mlsanity.com/paper/2302.13766/) (2023)
- [UniINR: Event-guided Unified Rolling Shutter Correction, Deblurring, and Interpolation](https://mlsanity.com/paper/2305.15078/) (2023)
- [Data-efficient Event Camera Pre-training via Disentangled Masked Modeling](https://mlsanity.com/paper/2403.00416/) (2024)
- [Learning to See Through with Events](https://mlsanity.com/paper/2212.02219/) (2022)

## Abstract (arXiv)

With extremely high temporal resolution, event cameras have a large potential for robotics and computer vision. However, their asynchronous imaging mechanism often aggravates the measurement sensitivity to noises and brings a physical burden to increase the image spatial resolution. To recover high-quality intensity images, one should address both denoising and super-resolution problems for event cameras. Since events depict brightness changes, with the enhanced degeneration model by the events, the clear and sharp high-resolution latent images can be recovered from the noisy, blurry and low-resolution intensity observations. Exploiting the framework of sparse learning, the events and the low-resolution intensity observations can be jointly considered. Based on this, we propose an explainable network, an event-enhanced sparse learning network (eSL-Net), to recover the high-quality images from event cameras. After training with a synthetic dataset, the proposed eSL-Net can largely improve the performance of the state-of-the-art by 7-12 dB. Furthermore, without additional training process, the proposed eSL-Net can be easily extended to generate continuous frames with frame-rate as high as the events.