# Event-assisted 12-stop HDR Imaging of Dynamic Scene

arXiv: https://arxiv.org/abs/2412.14705 · HTML: https://mlsanity.com/paper/2412.14705/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 66 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Learning to Predict Indoor Illumination from a Single Image](https://mlsanity.com/paper/1704.00090/) (2017)
- [From Sim-to-Real: Toward General Event-based Low-light Frame Interpolation with Per-scene Optimization](https://mlsanity.com/paper/2406.08090/) (2024)
- [Event-Based Motion Magnification](https://mlsanity.com/paper/2402.11957/) (2024)
- [HDR Reconstruction from Bracketed Exposures and Events](https://mlsanity.com/paper/2203.14825/) (2022)
- [HDR Imaging for Dynamic Scenes with Events](https://mlsanity.com/paper/2404.03210/) (2024)

## Built on by (most cited first, 2 of 2)

- [Event Camera Guided Visual Media Restoration & 3D Reconstruction: A Survey](https://mlsanity.com/paper/2509.09971/) (2025)
- [Dual-Exposure Imaging with Events](https://mlsanity.com/paper/2604.10273/) (2026)

## Abstract (arXiv)

High dynamic range (HDR) imaging is a crucial task in computational photography, which captures details across diverse lighting conditions. Traditional HDR fusion methods face limitations in dynamic scenes with extreme exposure differences, as aligning low dynamic range (LDR) frames becomes challenging due to motion and brightness variation. In this work, we propose a novel 12-stop HDR imaging approach for dynamic scenes, leveraging a dual-camera system with an event camera and an RGB camera. The event camera provides temporally dense, high dynamic range signals that improve alignment between LDR frames with large exposure differences, reducing ghosting artifacts caused by motion. Also, a real-world finetuning strategy is proposed to increase the generalization of alignment module on real-world events. Additionally, we introduce a diffusion-based fusion module that incorporates image priors from pre-trained diffusion models to address artifacts in high-contrast regions and minimize errors from the alignment process. To support this work, we developed the ESHDR dataset, the first dataset for 12-stop HDR imaging with synchronized event signals, and validated our approach on both simulated and real-world data. Extensive experiments demonstrate that our method achieves state-of-the-art performance, successfully extending HDR imaging to 12 stops in dynamic scenes.