3-D Representations for Hyperspectral Flame Tomography

TL;DR

This paper compares classical voxel-grid and neural representations for 3-D hyperspectral flame tomography, showing voxel-grid with total variation regularization achieves highest accuracy in simulated reconstructions.

Contribution

It provides a quantitative comparison between voxel-grid and neural representations for flame tomography, highlighting the effectiveness of voxel-grid with regularization.

Findings

Voxel-grid with total variation regularizer best reproduces ground-truth flames.

Voxel-grid approach reduces memory and runtime compared to neural methods.

Neural representations show promise but are not yet superior in this context.

Abstract

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.