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通过Zernike相对比显微镜与未经训练的神经网络进行相位检索

Zinan Zhou, Keiichiro Toda, Rikimaru Kurata

|June 14, 2025

概括

现在可以对相对照显微镜 (PCM) 图像进行定量分析. 这项研究使用未经训练的神经网络来提高相位检索精度,消除了手动参数调整,以获得更好的显微镜结果.

科学领域:

显微镜的使用方法
计算成像技术的成像
生物物理学的生物物理.

背景情况:

相对比显微镜 (PCM) 是一种用于可视化透明样本的广泛使用的技术.
然而,PCM图像本质上是定性图像,不会直接提供相位信息.
从PCM图像中检索相位的现有计算方法需要对调整参数进行广泛的手动调整.

研究的目的:

从PCM图像中开发一种更准确,更强大的定量阶段检索方法.
为了消除手动规范化参数调整在相位检索算法中的需求.
为了证明使用深度学习先验用于阶段检索的可行性.

主要方法:

使用未经训练的神经网络作为现有阶段检索框架内的图像前.
将增强方法应用于数值和实验相位对比显微镜图像.
将性能与传统相位检索技术进行比较.

主要成果:

在定量阶段检索中实现了显著提高的准确性和稳定性.
证明了成功地消除了手动调节参数调节的需要.
使用多种PCM数据集验证了该方法.

结论:

未训练的神经网络有效地作为相位检索的强大图像先验.
拟议的深度先验方法为定量PCM分析提供了更自动和可靠的方法.
这种技术有望在各种科学领域推进定量相位成像.

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