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估计噪音SEM:基于扫描电子显微镜图像的深度学习噪音估计的新框架

Sheikh Shah Mohammad Motiur Rahman¹, Michel Salomon², Sounkalo Dembélé³

¹Université Marie et Louis Pasteur, CNRS, institut FEMTO-ST, F-90000 Belfort, France,.

Ultramicroscopy

|July 2, 2025

概括

本研究介绍了EstimateNoiseSEM,这是一种用于扫描电子显微镜 (SEM) 图像中的自动噪声估计的深度学习框架. 它准确地识别了噪声类型和水平,这对于有效的图像消噪至关重要.

科学领域:

显微镜的使用方法
图像处理图像处理
人工智能的人工智能

背景情况:

扫描电子显微镜 (SEM) 产生依赖于停留时间的噪声 (高斯式,玛式),使图像无声化复杂化.
准确的噪声估计 (类型和水平) 对于有效的SEM图像消噪至关重要.

研究的目的:

介绍EstimateNoiseSEM,一个用于在SEM图像中自动估计噪声的框架.
开发一个多阶段的深度学习方法来分类噪音类型和预测噪音水平.

主要方法:

一个分类网络选择机制优化了分类方法.
一个分类阶段确定噪声类型 (高斯式或马式).
一个回归模型预测了噪音水平.

主要成果:

该框架在合成样本上实现了高达80%的准确性,精度,回忆和F1分数,用于Gaussian噪声分类.
高斯噪音水平预测的平方根误差为0.98+/-0.01.
高斯噪声分类的准确率达到97%,由于马噪声的不确定性,降至80%.

结论:

在SEM中,EstimateNoiseSEM提供了一种可靠的方法,用于在SEM中进行自动噪声估计.
该研究提供了对马噪声估计的见解,指导未来的深度学习技术.
准确的噪声表征对于改善SEM图像质量至关重要.

关键词:

深度学习是一种深度学习.噪音估计噪音估计扫描电子显微镜图像扫描电子显微镜图像

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