A deep learning approach for the fast generation of acoustic holograms
Qin Lin, Jiaqian Wang, Feiyan Cai, Rujun Zhang, Degang Zhao, Xiangxiang Xia, Jinping Wang, Hairong Zheng
Abstract
Acoustic holographic techniques are crucial in diverse applications, such as three-dimensional holographic display and particle manipulation. However, conventional methods for computer-generated acoustics holography rely heavily on iterative optimization algorithms, which are time-consuming and particularly hinder their capacity of generating a dynamic hologram in real time. Here, a deep learning approach based on U-Net is proposed to rapidly generate an acoustic hologram with optimal amplitude and phase maps. It is demonstrated that, after being trained with adequate data that are numerically synthesized by the pseudo-inverse method, the proposed deep learning approach can generate both amplitude and phase maps for new target images with an improved overall reconstruction quality. Remarkably, after the offline cost is compensated by a lower online cost for the proposed DL approach, the hologram generation speed is significantly accelerated by the proposed deep learning approach as compared with the pseudo-inverse method, especially for complicated or dynamic images. With the hierarchical feature learning capability and the fast online computational speed, the proposed deep learning approach can serve as a smart platform for rapidly generating complete maps of holograms for the sophisticated or dynamical target images, leading to the new possibility of real-time acoustic-hologram-based applications.