mlsanityData to 14 Oct 2026

Synergistic Event-SVE Imaging for Quantitative Propellant Combustion Diagnostics

arXiv 2603.25054 ↗First posted 26 Mar 2026cs.CVcited by 0

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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.

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.

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15 of 62 references.

References · 15

  1. 2019Event-based Vision: A Surveycited by 1,016
  2. 2016The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAMcited by 203
  3. 2019Events-to-Video: Bringing Modern Computer Vision to Event Camerascited by 193
  4. 2017DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairscited by 78
  5. 2019Unsupervised Domain Adaptation for Depth Prediction from Imagescited by 29
  6. 2023MEFLUT: Unsupervised 1D Lookup Tables for Multi-exposure Image Fusioncited by 22
  7. 2019Scene Segmentation-Based Luminance Adjustment for Multi-Exposure Image Fusioncited by 15
  8. 2023Event-based Background-Oriented Schlierencited by 11
  9. 2025Event-based multi-view photogrammetry for high-dynamic, high-velocity target measurementcited by 9
  10. 2025Simultaneous Enhancement and Noise Suppression under Complex Illumination Conditionscited by 5
  11. 2025Optimal Pose Guidance for Stereo Calibration in 3D Deformation Measurementcited by 4
  12. 2025Perceptual Region-Driven Infrared-Visible Co-Fusion for Extreme Scene Enhancementcited by 4
3 more
  1. 2026Fusion-Restoration Image Processing Algorithm to Improve the High-Temperature Deformation Measurementcited by 3
  2. 2026A DMD-Based Adaptive Modulation Method for High Dynamic Range Imaging in High-Glare Environmentscited by 3
  3. 2021PAS-MEF: Multi-exposure image fusion based on principal component analysis, adaptive well-exposedness and saliency mapcited by 2

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