Problem
Research questions and friction points this paper is trying to address.
flame tomography
3-D reconstruction
voxel-grid representation
neural representation
hyperspectral imaging
3-D Representations for Hyperspectral Flame Tomography
🤖 AI Summary
This study addresses the challenge of efficiently and accurately reconstructing three-dimensional thermochemical fields of flames from hyperspectral infrared camera data. For the first time, it quantitatively compares voxel-grid representations—augmented with total variation regularization—and continuous neural representations within a unified algorithmic framework for flame tomography. Synthetic observations are generated by solving the radiative transfer equation convolved with the instrument line shape function, and reconstructions are evaluated via ray tracing. The results demonstrate that the voxel-grid approach combined with total variation regularization achieves superior accuracy in reconstructing spatial distributions of temperature and species concentrations while substantially reducing memory consumption and computational time, thereby offering an optimal trade-off between computational efficiency and reconstruction fidelity.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
flame tomography
voxel-grid representation
neural representation
total-variation regularization
hyperspectral imaging
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