Three-dimensional rapid visualization of flame temperature field via compression and noise reduction of light field imaging

Light field (LF) imaging, as an emerging temperature measurement technique, can simultaneously capture the intensity and direction information of radiation in the combustion flow field and has recently attracted extensive attention. However, the dense sampling of the LF introduces a large amount of redundant information and makes the plenoptic data susceptible to noise pollution, which can significantly affect the performance of 3D temper-ature field reconstruction. To address this problem, a novel approach, i.e., light field compression and noise reduction (LFCNR), is proposed to extract the main features of the projection matrix and separate the noise -related and signal-related subspaces of the plenoptic data, thus improving the reconstruction accuracy and ef-ficiency. The performance of the proposed LFCNR method and the effect of the hyper-parameters on recon-structed quality are investigated thoroughly. It has been observed that the proposed LFCNR-based method for 3D temperature tomography imaging has higher accuracy, more efficiency, and better anti-noise ability compared to the well-established method. LFCNR can improve the reconstructed temperature accuracy by approximately 20%, and the calculation time is shortened to 10%. Priori smoothing is introduced into the optimization objective to constrain the fluctuation of the retrieval temperature, called LFCNR-PS, which can further improve the reconstructed accuracy of axisymmetric and non-axisymmetric fields about 1.79% and 1.23%. Furthermore, experiments of the LFCNR-based method are carried out to reconstruct the 3D temperature field of ethylene diffusion flames. The reconstructed temperature slices demonstrate that the flame topology can be clearly and rapidly identified at different depths. The efficiency and robustness of the proposed method can be applied to the harsh environment, which will be beneficial to the reliable operation of thermal equipment, the study of tur-bulent chemical reaction mechanisms, fuel utilization, and the optimization design of the engine combustion chamber.

Automated summary

Light field imaging is a promising temperature measurement technique that captures both intensity and direction information of radiation in the combustion flow field. This study proposes a novel method called light field compression and noise reduction (LFCNR) to improve the accuracy and efficiency of 3D temperature field reconstruction by extracting main features and separating noise-related and signal-related subspaces. Experimental results demonstrate that LFCNR achieves higher accuracy, efficiency, and noise robustness compared to existing methods and can be applied in harsh environments for temperature field reconstruction.