Bo Lin, Cai Huajun, Song Yang, Ji Yunjing, Li Zhenhua, He Anzhi
Opt Express. 2023 Nov 6;31(23):39182-39200. doi: 10.1364/OE.505992.
Current Background-oriented schlieren tomography (BOST) methods rely primarily on iterative algorithms for reconstruction. Before reconstruction, a weight projection matrix was generated by performing 3D ray tracing using the projection relationship between the cameras, depending on the camera calibration parameters and large weight projection matrix which introduce artifacts and greatly reduce computational efficiency in the reconstruction. Considering that CT reconstruction uses spatial projection sequences from multiple directions, this study draws inspiration from the Recurrent Neural network(RNN) and utilizes spatial correlation between adjacent projection data to propose a background-oriented schlieren reconstruction method based on a gated recurrent unit (GRU) neural network. First, the model architecture is designed and implemented. Subsequently, numerical simulations were conducted using a methane combustion model to evaluate the proposed method, which achieved an average mean relative error (MRE) of 0.23%. Finally, reconstruction experiments were performed on the actual flow-field data above a candle flame, with a reprojection correlation coefficient of 89% and an average reconstruction time of only 1.04 s per frame. The results demonstrate that the proposed method outperforms traditional iterative reconstruction methods in terms of reconstruction speed and accuracy. This provides a feasible solution for the real-time reconstruction of three-dimensional instantaneous flow fields.
当前的背景纹影层析成像（BOST）方法主要依赖于迭代算法进行重建。在重建之前，通过利用相机之间的投影关系进行三维光线追踪来生成权重投影矩阵，这取决于相机校准参数以及会引入伪影并大大降低重建计算效率的大权重投影矩阵。考虑到CT重建使用来自多个方向的空间投影序列，本研究从循环神经网络（RNN）中获得灵感，并利用相邻投影数据之间的空间相关性，提出了一种基于门控循环单元（GRU）神经网络的背景纹影重建方法。首先，设计并实现了模型架构。随后，使用甲烷燃烧模型进行了数值模拟，以评估所提出的方法，该方法实现了0.23%的平均平均相对误差（MRE）。最后，对蜡烛火焰上方的实际流场数据进行了重建实验，重投影相关系数为89%，每帧平均重建时间仅为1.04秒。结果表明，所提出的方法在重建速度和准确性方面优于传统的迭代重建方法。这为三维瞬时流场的实时重建提供了一种可行的解决方案。
