Acoustic Hologram Reconstruction With Unsupervised Neural Network - Li, Boyi; Lu, Mengyang; Liu, Chengcheng; Liu, Xin; Ta, Dean (Frontiers in Materials, 2022)
Acoustic hologram is crucial in various acoustics applications. The reconstruction accuracy of acoustic field from hologram is important for determining performance of acoustic hologram system. However, challenges remain in acoustic hologram reconstruction where the conventional reconstruction methods generally lack in accuracy, complexity, and flexibility. Although the deep-learning (DL) based method has been used to overcome these limitations, it needs the labeled training data to optimize the network with a supervised strategy. To address the problem, we put forward a new unsupervised DL-based reconstruction method in this work, termed as PhysNet-AH, which is implemented by integrating a convolutional neural network with a physical model representing the process of acoustics hologram formation. The results demonstrate that we only need to provide PhysNet-AH a single acoustic field recorded from hologram, the network parameters can be optimized automatically without the labeled training data, and finally implement the acoustic hologram reconstruction with a high accuracy, in terms of SSIM and MSE indicators. Furthermore, with the trained model, the robustness and generalization capability of PhysNet-AH have also been well demonstrated by reconstructing the acoustic fields from different diffraction distances or different dataset. As a result, PhysNet-AH opens the door for fast, accuracy, flexible acoustic hologram-based applications.