对比神经网络与体素网格，发现后者在火焰重建中更高效准确。
3-D Representations for Hyperspectral Flame Tomography
- 用体素网格加总变差正则化实现三维火焰重建
- 体素法在精度、内存和速度上均优于神经网络表示
- 适合需要高精度与低资源消耗的实验仿真场景
火焰断层成像可通过三维热化学重构从实验中提取大量数据。近年来，基于神经网络的火焰表示方法相比传统方法表现出更高重建质量，但尚未在相同算法下与体素网格表示进行严格定量比较。本文将经典体素网格表示（采用不同正则化）与连续神经表示用于模拟池火的断层重建。两种表示均输出位置相关的温度与组分，随后通过射线追踪求解辐射传输方程，计算入射至高光谱红外相机的光谱强度，并与仪器线型函数卷积。结果表明，采用总变差正则化的体素网格方法在保持最低内存占用和运行时间的前提下，对合成火焰的真实分布再现精度最高。未来工作将探索更多表示形式及真实实验配置。
原文摘要 · Abstract (English)
Flame tomography is a compelling approach for extracting large amounts of data from experiments via 3-D thermochemical reconstruction. Recent efforts employing neural-network flame representations have suggested improved reconstruction quality compared with classical tomography approaches, but a rigorous quantitative comparison with the same algorithm using a voxel-grid representation has not been conducted. Here, we compare a classical voxel-grid representation with varying regularizers to a continuous neural representation for tomographic reconstruction of a simulated pool fire. The representations are constructed to give temperature and composition as a function of location, and a subsequent ray-tracing step is used to solve the radiative transfer equation to determine the spectral intensity incident on hyperspectral infrared cameras, which is then convolved with an instrument lineshape function. We demonstrate that the voxel-grid approach with a total-variation regularizer reproduces the ground-truth synthetic flame with the highest accuracy for reduced memory intensity and runtime. Future work will explore more representations and under experimental configurations.
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