Synergistic Event-SVE Imaging for Quantitative Propellant Combustion Diagnostics

Jing Tao; Taihang Lei; Banglei Guan; Ying Qu; Xudong Na; Likun Ma; Yang Shang; Qifeng Yu

cs.CVMar 26, 2026

Synergistic Event-SVE Imaging for Quantitative Propellant Combustion Diagnostics

Authors: Jing Tao, Taihang Lei, Banglei Guan, Ying Qu, Xudong Na, Likun Ma, Yang Shang, Qifeng Yu

Organizations: College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China · Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation, National University of Defense Technology, Changsha 410073, China · Hypersonic Technology Laboratory, National University of Defense Technology, Changsha 410073, China

Abstract

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.

Figures & tables

Fig. 1: Schematic diagram of optical measurement system.

Fig. 2: Probabilistic smoke-likelihood map $F(x)$ , with the color bar indicating a normalized visibility score (higher implies stronger smoke influence and reduced visibility).

Fig. 3: Particle Imaging Extraction Strategy Diagram.

Fig. 4: Propellant combustion stereo imaging model.

Fig. 5: Physical Scale Calculation Model.

Fig. 6: SVE HDR display: (a) Comparison of results before and after fusion. (b) Multi-state tracking of specific particles. (c) Comparison of indicators before and after fusion.

Fig. 7: Quantitative feature preservation evaluation. (a) Combustion scene with five Regions of Interest (ROIs): 1) Flame Core, 2) Combusting Particles, 3) Extinguished Particles, 4) Smoke Region, 5) Propellant Region. (b) Average Gradient (AG) comparison of different methods across these ROIs.

Region of Interest	Best Single Exp.	PESPD-MEF [ 62 ]	Our Method
ROI 1 (Flame Core)	1.180	1.787	2.250
ROI 2 (Combusting Particles)	9.886	9.009	10.664
ROI 3 (Extinguished Particles)	3.346	2.505	5.690
ROI 4 (Smoke Region)	2.562	2.279	2.216
ROI 5 (Propellant Region)	1.811	1.116	3.187

Table 1: Average Gradient (AG) values for different methods across selected ROIs. Bold values indicate the optimal result.

Fig. 8: Overview of stereo event-camera calibration and validation: (a) Extrinsic parameters of the stereo event-camera observation. (b) Stereo triangulation accuracy validation using reference scale targets. Indices 1–10 denote ten independent calibration scale targets with known physical sizes used for accuracy validation.

Event Cameras	Left Camera	Right Camera
Focal Length (pixels)	$1703.25$	$1959.47$
Principal Point (pixels)	$(582.69,497.77)$	$(502.29,387.05)$
Distortion Parameter	$(-0.11,0.22,-0.01,0.01,0.00)$	$(0.13,-5.68,-0.02,0.01,0.00)$
Euler Angle $(\mathrm{rad})$	$(0,0,0)$	$(-0.360,1.410,0.171)$
Translation Vector $(\mathrm{mm})$	$(0,0,0)$	$(-105.90,-28.25,149.75)$

Table 2: Calibration results of our stereo event-camera observation system.

AE Mean (mm)	AE STD (mm)	RE Mean (%)	RE STD (%)
$0.0659$	$0.1554$	$0.3307$	$0.2067$

Table 3: Measurement error of our stereo-event camera observation system.

Fig. 9: Stereo event-camera observation of particle monitoring.

Fig. 10: Particle separation monitoring: (a) measurement results from the left event camera; (b) measurement results from the right event camera. The dotted line indicates the particle separation time point.

Fig. 11: Equivalent particle size measurement results: (a) SVE camera and left event camera; (b) high-speed camera and right event camera.

Fig. 12: Stereo event-camera particle tracking and size analysis. (a) Stereo event-camera observation of particles and matching effects. (b) Equivalent particle size measurement results.

Computational Imaging

Computational imaging combines optical and sensor design with algorithms to recover or infer scene information from indirect, compressed, or noisy measurements.

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High Dynamic Range Imaging Based on an Asymmetric Event-SVE Camera System

Pengju Sun, Banglei Guan, Jing Tao, Zhenbao Yu+3

College of Aerospace Science and Engineering, National University of Defense Technology, Hunan 410073, China · Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation, Hunan 410073, China

HDR Imaging Multi-Source Image Fusion Event-Based Vision Cross-Modal Image Registration

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High dynamic range (HDR) imaging under extreme illumination remains challenging for conventional cameras due to overexposure. Event cameras provide microsecond temporal resolution and high dynamic range, while spatially varying exposure (SVE) sensors offer single-shot radiometric diversity.We present a hardware--algorithm co-designed HDR imaging system that tightly integrates an SVE micro-attenuation camera with an event sensor in an asymmetric dual-modality configuration. To handle non-coaxial geometry and heterogeneous optics, we develop a two-stage cross-modal alignment framework that combines feature-guided coarse homography estimation with a multi-scale refinement module based on spatial pooling and frequency-domain filtering. On top of aligned representations, we develop a cross-modal HDR reconstruction network with convolutional fusion, mutual-information regularization, and a learnable fusion loss that adaptively balances intensity cues and event-derived structural constraints. Comprehensive experiments on both synthetic benchmarks and real captures demonstrate that the proposed system consistently improves highlight recovery, edge fidelity, and robustness compared with frame-only or event-only HDR pipelines. The results indicate that jointly optimizing optical design, cross-modal alignment, and computational fusion provides an effective foundation for reliable HDR perception in highly dynamic and radiometrically challenging environments.

Abstract

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