# Learning to Super Resolve Intensity Images from Events

arXiv: https://arxiv.org/abs/1912.01196 · HTML: https://mlsanity.com/paper/1912.01196/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2019.

## Built on by (most cited first, 26 of 26)

- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [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)
- [Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation](https://mlsanity.com/paper/2109.02618/) (2021)
- [HyperE2VID: Improving Event-Based Video Reconstruction via Hypernetworks](https://mlsanity.com/paper/2305.06382/) (2023)
- [Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image Translation](https://mlsanity.com/paper/2109.01801/) (2021)
- [Event-guided Deblurring of Unknown Exposure Time Videos](https://mlsanity.com/paper/2112.06988/) (2021)
- [EventAid: Benchmarking Event-aided Image/Video Enhancement Algorithms with Real-captured Hybrid Dataset](https://mlsanity.com/paper/2312.08220/) (2023)
- [EventHDR: from Event to High-Speed HDR Videos and Beyond](https://mlsanity.com/paper/2409.17029/) (2024)
- [Unsupervised Domain Adaptation for Training Event-Based Networks Using Contrastive Learning and Uncorrelated Conditioning](https://mlsanity.com/paper/2303.12424/) (2023)
- [SRFNet: Monocular Depth Estimation with Fine-grained Structure via Spatial Reliability-oriented Fusion of Frames and Events](https://mlsanity.com/paper/2309.12842/) (2023)
- [Temporal Event Stereo via Joint Learning with Stereoscopic Flow](https://mlsanity.com/paper/2407.10831/) (2024)
- [Learning to Super-Resolve Blurry Images with Events](https://mlsanity.com/paper/2302.13766/) (2023)
- [Superevents: Towards Native Semantic Segmentation for Event-based Cameras](https://mlsanity.com/paper/2105.06091/) (2021)
- [Multi-domain Collaborative Feature Representation for Robust Visual Object Tracking](https://mlsanity.com/paper/2108.04521/) (2021)
- [Self-Supervised Intensity-Event Stereo Matching](https://mlsanity.com/paper/2211.00509/) (2022)
- [HDR Reconstruction from Bracketed Exposures and Events](https://mlsanity.com/paper/2203.14825/) (2022)
- [Temporal Up-Sampling for Asynchronous Events](https://mlsanity.com/paper/2208.08721/) (2022)
- [SpikeNVS: Enhancing Novel View Synthesis from Blurry Images via Spike Camera](https://mlsanity.com/paper/2404.06710/) (2024)
- [Fast Window-Based Event Denoising with Spatiotemporal Correlation Enhancement](https://mlsanity.com/paper/2402.09270/) (2024)
- [Event-Enhanced Snapshot Compressive Videography at 10K FPS](https://mlsanity.com/paper/2404.07551/) (2024)
- [EvPlug: Learn a Plug-and-Play Module for Event and Image Fusion](https://mlsanity.com/paper/2312.16933/) (2023)
- [An Event-Oriented Diffusion-Refinement Method for Sparse Events Completion](https://mlsanity.com/paper/2401.03153/) (2024)
- [EV-MGDispNet: Motion-Guided Event-Based Stereo Disparity Estimation Network with Left-Right Consistency](https://mlsanity.com/paper/2408.05452/) (2024)
- [Removing Blocking Artifacts in Video Streams Using Event Cameras](https://mlsanity.com/paper/2105.05973/) (2021)
- [Relating Events and Frames Based on Self-Supervised Learning and Uncorrelated Conditioning for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2401.01042/) (2024)

## Abstract (arXiv)

An event camera detects per-pixel intensity difference and produces asynchronous event stream with low latency, high dynamic range, and low power consumption. As a trade-off, the event camera has low spatial resolution. We propose an end-to-end network to reconstruct high resolution, high dynamic range (HDR) images directly from the event stream. We evaluate our algorithm on both simulated and real-world sequences and verify that it captures fine details of a scene and outperforms the combination of the state-of-the-art event to image algorithms with the state-of-the-art super resolution schemes in many quantitative measures by large margins. We further extend our method by using the active sensor pixel (APS) frames or reconstructing images iteratively.