Real-Time Acoustic Holography for Target Pattern Acoustic Field Reconstruction With Iterative Unsupervised Learning

The ability to reconstruct acoustic field is essential for applications such as particle manipulation, medical therapy, and ultrasonic imaging. Conventional acoustic field reconstruction algorithms are limited in both speed and accuracy when generating acoustic holograms, which further constrains the practical applications of acoustic holography. In this study, we propose an acoustic field reconstruction method that achieves both speed and accuracy by leveraging an acoustic model tailored for diffraction. A unit transducer is integrated with an acoustic lens, utilizing lens thickness and acoustic velocity variations to produce accurate phase maps at specific frequencies. To reduce the dependence on labeled datasets, we propose a deep learning framework with an autoencoder structure for unsupervised training without relying on any a priori information. Additionally, a novel loss function is designed for the framework to realize the energy efficiency constraint. The proposed method is compared with traditional Angular Spectrum Method (ASM) and Automatic Differentiation (Diff-PAT). Experimental results show that the Peak Signal-to-Noise Ratio (PSNR) of this method reaches 21.59, which is 24.1% higher than that of ASM and 19.2% higher than that of Diff-PAT. After network training, the proposed method generates phase holograms hundreds of times faster than ASM and Diff-PAT. Under the same network architecture, our newly designed loss function improves energy efficiency to 2.1 times that of the original method.