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Abstract
Classic algorithms for computed tomography of chemiluminescence include two main steps: tomographic weight matrix calculation using imaging models, and inverse calculation using algebraic reconstruction techniques (ARTs). However, pre-calculated weight matrices require a large amount of storage, and accurate voxel weights may not be obtained using a simplified imaging model. In this study, we propose a new, to the best of our knowledge, method named the multi-weight encode reconstruction network (Multi-WERNet) to learn the implicit light propagation physics from the multi-projections of different flames and simultaneously reconstruct the 3D flame chemiluminescence. The reconstructed results from Multi-WERNet are close to those of ART, and no radial streak is found, which is commonly seen in ART-based methods. With the help of information from different flames, the results reconstructed with 5 views using Multi-WERNet outperform the ART method. Moreover, Multi-WERNet successfully learns the implicit light propagation physics as a voxel weight encoder and can be transferred to unseen cases. Finally, Multi-WERNet is found to have higher robustness than ART in reconstruction with imperfect projections, which makes the algorithm more practical.
© 2021 Optical Society of America
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