Physically enhanced neural network based on projection map synthesis for chemiluminescence tomography reconstruction with limited views

In many combustion systems, data collection through optical windows is often hindered by fixed mechanical components that limit the number of available observation angles. This restriction poses a significant challenge to the accurate reconstruction of chemiluminescence tomography images with limited views. To address this limitation, we propose a novel projection interpolation approach for physically enhanced neural networks (PIPEN) to address this limitation. The PIPEN utilizes projection maps from two intersecting viewpoints and employs an interpolation network to estimate the projection maps for occluded views between these two angles. The interpolated and original projections are input into a physically enhanced neural network (PENN) to perform a volumetric tomography reconstruction. The PENN was designed to accommodate practical scenarios in which ground-truth data are unavailable. Furthermore, the loss function in PENN is enhanced with a total variation (TV) regularization term that mitigates noise and artifacts and improves the quality of the visual reconstruction. Experimental evaluations indicate that the PIPEN achieves a reconstruction performance comparable to that using a complete set of seven directions despite only utilizing projection maps from two orthogonal views. These results suggest that the PIPEN has significant potential for practical 3D flame reconstruction under constrained observation conditions.