Affiliation:
1. School of Information Science and Technology, Shanghaitech University 1 , Shanghai 201210, China
2. Institute of Automation, Chinese Academy of Science 2 , Beijing 100190, China
3. Shanghai Engineering Research Center of Intelligent Vision and Imaging 3 , Shanghai 201210, China
Abstract
Acoustic holography (AH) provides a promising technique for arbitrary acoustic field reconstruction, supporting many applications like robotic micro-nano manipulation, neuromodulation, volumetric imaging, and virtual reality. In AH, three-dimensional (3D) acoustic fields quantified with complex-valued acoustic pressures are reconstructed by virtue of two-dimensional (2D) acoustic holograms. Phase-only hologram (POH) is recently regarded as an energy-efficient way for AH, which is typically implemented by a dynamically programmable phased array of transducers (PATs). As a result, spatiotemporal precise acoustic field reconstruction is enabled by precise, dynamic, and individual actuation of PAT. Thus, 2D POH is required per arbitrary acoustic fields, which can be viewed as a physical inverse problem. However, solving the aforementioned physical inverse problem in numerical manners poses challenges due to its non-linear, high-dimensional, and complex coupling natures. The existing iterative algorithms like the iterative angular spectrum approach (IASA) and iterative backpropagation (IB) still suffer from speed-accuracy trade-offs. Hence, this paper explores a novel physics-iterative-reinforced deep learning method, in which frequency-argument contrastive learning is proposed facilitated by the inherent physical nature of AH, and the energy conservation law is under consideration. The experimental results demonstrate the effectiveness of the proposed method for acoustic field reconstruction, highlighting its significant potential in the domain of acoustics, and pushing forward the combination of physics into deep learning.
Funder
National Natural Science Foundation of China
Shanghai Pujiang Talents Program