Acoustic Hologram Reconstruction With Unsupervised Neural Network

Abstract

An acoustic hologram is crucial in various acoustics applications. The reconstruction accuracy of the acoustic field from the hologram is important for determining the performance of the acoustic hologram system. However, challenges remain in acoustic hologram reconstruction where the conventional reconstruction methods generally lack 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 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 with a single acoustic field recorded from the hologram, the network parameters can be optimized automatically without the labeled training data, and finally implement the acoustic hologram reconstruction with high accuracy, in terms of SSIM and mean squared error 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 datasets. As a result, PhysNet-AH opens the door for fast, accurate, and flexible acoustic hologram–based applications.