A Hardware-Algorithm Co-Designed Framework for HDR Imaging and Dehazing in Extreme Rocket Launch Environments

2026-01-13

AI Summary

This study addresses the challenge of optical measurement degradation in extreme rocket launch environments, where intense combustion generates high dynamic range illumination (>120 dB) and dense particulate haze that severely obscure critical mechanical parameters. To overcome this, the work proposes a hardware–algorithm co-design approach that integrates a custom spatially varying exposure (SVE) sensor with a physics-aware dehazing algorithm that operates without requiring an ideal atmospheric model. By capturing multi-exposure information in a single shot, the method enables region-adaptive illumination optimization and multi-scale entropy-constrained fusion. This effectively disentangles haze from true radiance, successfully reconstructing physically accurate visual data of plume and engine regions in both real launch imagery and controlled experiments, thereby enabling high-precision extraction of key parameters such as particle velocity, flow instability frequency, and structural vibration.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Algorithm Configuration
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Quantitative optical measurement of critical mechanical parameters -- such as plume flow fields, shock wave structures, and nozzle oscillations -- during rocket launch faces severe challenges due to extreme imaging conditions. Intense combustion creates dense particulate haze and luminance variations exceeding 120 dB, degrading image data and undermining subsequent photogrammetric and velocimetric analyses. To address these issues, we propose a hardware-algorithm co-design framework that combines a custom Spatially Varying Exposure (SVE) sensor with a physics-aware dehazing algorithm. The SVE sensor acquires multi-exposure data in a single shot, enabling robust haze assessment without relying on idealized atmospheric models. Our approach dynamically estimates haze density, performs region-adaptive illumination optimization, and applies multi-scale entropy-constrained fusion to effectively separate haze from scene radiance. Validated on real launch imagery and controlled experiments, the framework demonstrates superior performance in recovering physically accurate visual information of the plume and engine region. This offers a reliable image basis for extracting key mechanical parameters, including particle velocity, flow instability frequency, and structural vibration, thereby supporting precise quantitative analysis in extreme aerospace environments.

Problem

• HDR imaging
• dehazing
• rocket launch
• extreme environments
• quantitative optical measurement

Innovation

• Hardware-Algorithm Co-Design
• Spatially Varying Exposure (SVE)
• Physics-Aware Dehazing
• High Dynamic Range Imaging
• Extreme Environment Vision