Rapid Reconstruction of Acoustic Holograms Using Improved IU-Net With Adaptive Learning Rate Scheduling

The method of physically recording acoustic holograms is time-consuming and inefficient, hindering the ability to reconstruct acoustic holograms rapidly. This text aims to propose a deep learning strategy based on an improved IU-Net to rapidly reconstruct acoustic holograms with phase information. The angular spectrum method is used to simulate the phase information distribution of different sound sources, positions, and initial angles, thereby quickly generating the required data samples, especially for complex or dynamic image scenarios. The framework employs a custom learning rate adjustment mechanism to dynamically adjust the learning rate in real-time, adapt to the training process, and support a stepwise learning strategy. Experimental results show that the proposed method achieves an accuracy of 90.9% in acoustic hologram reconstruction, with a Dice coefficient of 0.928, improving reconstruction speed by over 30% compared to traditional methods, ensuring high-quality and rapid reconstruction of acoustic holograms.