# Synergistic Event-SVE Imaging for Quantitative Propellant Combustion Diagnostics

arXiv: https://arxiv.org/abs/2603.25054 · HTML: https://mlsanity.com/paper/2603.25054/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 62 references that are in the map.
- First posted on arXiv 26 Mar 2026.

## Builds on (most cited first, 15 of 15)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](https://mlsanity.com/paper/1610.08336/) (2016)
- [Events-to-Video: Bringing Modern Computer Vision to Event Cameras](https://mlsanity.com/paper/1904.08298/) (2019)
- [DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs](https://mlsanity.com/paper/1712.07384/) (2017)
- [Unsupervised Domain Adaptation for Depth Prediction from Images](https://mlsanity.com/paper/1909.03943/) (2019)
- [MEFLUT: Unsupervised 1D Lookup Tables for Multi-exposure Image Fusion](https://mlsanity.com/paper/2309.11847/) (2023)
- [Scene Segmentation-Based Luminance Adjustment for Multi-Exposure Image Fusion](https://mlsanity.com/paper/1903.07428/) (2019)
- [Event-based Background-Oriented Schlieren](https://mlsanity.com/paper/2311.00434/) (2023)
- [Event-based multi-view photogrammetry for high-dynamic, high-velocity target measurement](https://mlsanity.com/paper/2506.00578/) (2025)
- [Simultaneous Enhancement and Noise Suppression under Complex Illumination Conditions](https://mlsanity.com/paper/2512.08378/) (2025)
- [Optimal Pose Guidance for Stereo Calibration in 3D Deformation Measurement](https://mlsanity.com/paper/2511.18317/) (2025)
- [Perceptual Region-Driven Infrared-Visible Co-Fusion for Extreme Scene Enhancement](https://mlsanity.com/paper/2512.06400/) (2025)
- [Fusion-Restoration Image Processing Algorithm to Improve the High-Temperature Deformation Measurement](https://mlsanity.com/paper/2601.12682/) (2026)
- [A DMD-Based Adaptive Modulation Method for High Dynamic Range Imaging in High-Glare Environments](https://mlsanity.com/paper/2602.12044/) (2026)
- [PAS-MEF: Multi-exposure image fusion based on principal component analysis, adaptive well-exposedness and saliency map](https://mlsanity.com/paper/2105.11809/) (2021)

## Abstract (arXiv)

Real-time monitoring of high-energy propellant combustion is difficult. Extreme high dynamic range (HDR), microsecond-scale particle motion, and heavy smoke often occur together. These conditions drive saturation, motion blur, and unstable particle extraction in conventional imaging. We present a closed-loop Event--SVE measurement system that couples a spatially variant exposure (SVE) camera with a stereo pair of neuromorphic event cameras. The SVE branch produces HDR maps with an explicit smoke-aware fusion strategy. A multi-cue smoke-likelihood map is used to separate particle emission from smoke scattering, yielding calibrated intensity maps for downstream analysis. The resulting HDR maps also provide the absolute-intensity reference missing in event cameras. This reference is used to suppress smoke-driven event artifacts and to improve particle-state discrimination. Based on the cleaned event observations, a stereo event-based 3D pipeline estimates separation height and equivalent particle size through feature extraction and triangulation (maximum calibration error 0.56%). Experiments on boron-based propellants show multimodal equivalent-radius statistics. The system also captures fast separation transients that are difficult to observe with conventional sensors. Overall, the proposed framework provides a practical, calibration-consistent route to microsecond-resolved 3D combustion measurement under smoke-obscured HDR conditions.