Three-Dimensional Reconstruction of Dilute Bubbly Flow Field With Light-Field Images Based on Deep Learning Method
Three-Dimensional Reconstruction of Dilute Bubbly Flow Field With Light-Field Images Based on Deep Learning Method
复制标题
基于深度学习方法的稀相泡状流场光场图像三维重建
DOI：
10.1109/jsen.2021.3065374
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发表时间：
2021-06
影响因子：
4.3
通讯作者：
Hongyi Wang;Hongyu Wang;Xinjun Zhu;Limei Song;Qinghua Guo;Feng Dong
中科院分区：
文献类型：
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作者：
Hongyi Wang;Hongyu Wang;Xinjun Zhu;Limei Song;Qinghua Guo;Feng Dong
气泡流场的三维重建对于研究气液两相流的运动具有重要意义。结合光场图像中丰富的气泡信息，提出了一种基于深度学习算法DIF-LENet(Double Information Fusion With LeNet5 Net)的稀相气泡流三维重建方法。详细介绍了用于数据处理的光场图像的标定方法和气泡分割算法。DIF-LENet实现了不同维度数据的融合和回归预测。利用DIF-LENet算法，将重聚焦后的气泡图像与重聚焦图像的焦距进行融合，即可预测气泡深度。然后根据气泡深度、气泡中心和气泡直径重建三维气泡场。与基于统计的重建方法和基于LeNet5的重建方法相比，基于DIF-LeNet的重建方法在重建精度和速度上都有所提高。尤其是对于重聚焦图像序列中没有聚焦图像的气泡，基于DIF-LeNet的三维重建效果尤为突出。
The three-dimensional reconstruction of bubble flow field is of great significance to study the motion of gas-liquid two phase flow. Combined with the abundant information of bubbles in the light field images, a fast and accurate 3D reconstruction method for dilute bubbly flow based on the deep learning algorithm DIF-LeNet (Double Information Fusion with LeNet5 net) is proposed in this work. The calibration method and bubble segmentation algorithm of light field image for data processing is detailed. DIF-LeNet realizes the fusion of different dimension data and regression prediction. By DIF-LeNet, bubble depth could be predicted by fusing the refocused bubble image with the focal distance of the refocused image. Then, the three-dimensional bubble field could be reconstructed with the bubble depth, bubble center and bubble diameter. Comparing with the reconstruction method based on statistics and LeNet5, the reconstruction accuracy and speed of the proposed method based on DIF-LeNet are improved. Especially for the bubble without focused image in the refocused image sequence, the effect of 3D reconstruction based on DIF-LeNet is much prominent.