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神经图像化:用于体积波面传感和成像的框架

Casey J Pellizzari, Adrienne M Weaver, Tyler J Hardy

|August 12, 2025

概括

神经图像敏化 (NIS) 使用神经网络在数字全息测量中纠正来自大气动荡的相位错误. 这种新的方法增强了体积波面传感和成像,优于传统技术.

科学领域:

光学物理学的光学物理.
计算机成像成像技术
波浪前线传感波浪前线传感

背景情况:

大气动荡会导致光学测量中的分布体积偏差.
数字全息 (DH) 测量容易发生异平面相位错误.
传统的图像利 (IS) 方法与复杂的相位偏差作斗争.

研究的目的:

引入一个新的框架,神经图像利 (NIS),用于纠正相位错误.
利用隐性神经表示来进行体积波面传感和成像.
在受到流影响的数字全息测量中展示NIS的有效性.

主要方法:

使用无监督学习与一次性DH数据.
利用隐式神经表示来感知和纠正异平面相位错误.
使用波光学模拟和实验室实验验证实NIS.

主要成果:

NIS成功地通过流进行了体积波面传感和成像.
与传统的IS框架相比,实现了更高的峰值Strehl比率.
在微弱,中度和深度流条件下表现出卓越的性能.

结论:

尼斯是体积波面传感和成像的一个有前途的方法.
在动荡环境中克服基于像素的IS框架的局限性.
在显微镜,计量学,激光雷达和遥感领域具有广泛的应用.

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