深度声场消噪器:使用深度神经网络进行光学测量声场消噪
Optics express
|October 20, 2023
概括
一个新的深度神经网络 (DNN) 有效地消除了光学测量的声场图像,克服了光学干扰测量中的噪声限制. 与传统方法相比,这种先进的深度声场消毒器显著提高了声学成像的准确性.
科学领域:
- 声学 声学 在声学方面
- 光学物理学 光学物理学
- 机器学习 机器学习
背景情况:
- 光学方法提供超越传统声学传感器的高空间分辨率声场成像.
- 光学干扰测量测量具有低灵敏度,导致声音场图像中的噪音很大.
- 现有的消噪技术很难有效地消除这些复杂的光学测量中的噪声.
研究的目的:
- 开发一种基于深度神经网络 (DNN) 的方法,用于对光学测量的声场图像进行无声化.
- 提高声学现象的声场成像质量和准确性.
- 为在具有挑战性的光学声学测量中提供强大的降噪解决方案.
主要方法:
- 时间变化的声场图像序列被分解成的复杂幅度图像,使用时间方向的里埃变换.
- 复杂的图像被转换成双通道的真实和虚拟部分进行处理.
- 一个没有非线性激活的深度神经网络在模拟的声学数据上进行了训练,以消除噪音.
主要成果:
- 拟议的深度声场消毒器在传统的图像过器和时空过器上表现出优越的性能.
- 基于DNN的方法在处理任务方面明显优于其他深度学习架构.
- 在数值模拟和实验数据 (并行相位移干扰测量,全息斑干扰测量) 上的验证证实了有效性.
结论:
- 拟议的深度声场消噪器为光学声场成像的降噪提供了显著的进步.
- 这种基于DNN的方法克服了传统方法的局限性,可以实现更清晰的声学可视化.
- 该研究为声学和光学计量学领域的研究人员和从业人员提供了宝贵的工具.
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