基于物理的生成对抗网络,用于实时声学全息
Qingyi Lu1, Chengxi Zhong1, Hu Su2
1School of Information Science and Technology, Shanghaitech University, Shanghai 201210, China.
Ultrasonics
|February 2, 2025
概括
本研究介绍了用于仅相位全息 (POH) 的深度学习算法,以实现高可靠性,实时声场重建. 具有Y-Net结构的新型软-GAN显著提高了全息图生成准确度.
科学领域:
- 声学 声学 在声学方面
- 全息影像的使用方法.
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 声学全息 (AH) 将复杂的声场编码为2D全息图.
- 只有相位全息 (POH) 提供优越的信息量和存储效率.
- 现有的POH算法缺乏高保真性和实时性能.
研究的目的:
- 开发一种深度学习算法,用于准确和实时的POH计算.
- 通过基于物理的方法来解决当前POH方法的局限性.
主要方法:
- 一个深度学习算法,将角光谱方法 (ASM) 与称为软GAN的生成对抗网络 (GAN) 结合起来.
- 实施一个Y-Net结构,用于频率和空间域的双解码器来处理高频特征.
主要成果:
- 实现了24.05dB的最先进的峰值信号噪声比率 (PSNR).
- 证明了准确和实时的声学全息图重建.
- 拟议的方法显示了实际应用的巨大潜力.
结论:
- 基于物理的深度学习方法有效地学习POH生成的反向映射.
- 软GAN和Y-Net结构克服了POH的忠诚度和速度的限制.
- 这种方法为先进的动态声学操纵铺平了道路.
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