Xu Wenjiang, Luo Weiyi, Wang Yu, You Yancheng
Appl Opt. 2020 Jul 1;59(19):5729-5736. doi: 10.1364/AO.392803.
Three-dimensional (3D) computed tomography (CT) is becoming a well-established tool for turbulent combustion diagnostics. However, the 3D CT technique suffers from contradictory demands of spatial resolution and domain size. This work therefore reports a data-driven 3D super-resolution approach to enhance the spatial resolution by two times along each spatial direction. The approach, named 3D super-resolution generative adversarial network (3D-SR-GAN), builds a generator and a discriminator network to learn the topographic information and infer high-resolution 3D turbulent flame structure with a given low-resolution counterpart. This work uses numerically simulated 3D turbulent jet flame structures as training data to update model parameters of the GAN network. Extensive performance evaluations are then conducted to show the superiority of the proposed 3D-SR-GAN network, compared with other direct interpolation methods. The results show that a convincing super-resolution (SR) operation with the overall error of ∼4 and the peak signal-to-noise ratio of 37 dB can be reached with an upscaling factor of 2, representing an eight times enhancement of the total voxel number. Moreover, the trained network can predict the SR structure of the jet flame with a different Reynolds number without retraining the network parameters.
三维（3D）计算机断层扫描（CT）正成为一种成熟的湍流燃烧诊断工具。然而，3D CT技术面临着空间分辨率和区域大小相互矛盾的要求。因此，这项工作报告了一种数据驱动的3D超分辨率方法，可在每个空间方向上将空间分辨率提高两倍。该方法名为3D超分辨率生成对抗网络（3D-SR-GAN），它构建了一个生成器和一个判别器网络，以学习地形信息并根据给定的低分辨率对应物推断高分辨率的3D湍流火焰结构。这项工作使用数值模拟的3D湍流射流火焰结构作为训练数据来更新GAN网络的模型参数。然后进行了广泛的性能评估，以展示所提出的3D-SR-GAN网络相对于其他直接插值方法的优越性。结果表明，在放大因子为2的情况下，可以实现令人信服的超分辨率（SR）操作，总误差约为4，峰值信噪比为37 dB，这意味着体素总数增加了八倍。此外，经过训练的网络可以在不重新训练网络参数的情况下预测不同雷诺数的射流火焰的SR结构。
