卷积神经网络在聚焦离子束扫描电子显微镜图像消极化中的应用
Wen Xu1,2,3, Dong Zhao1,2,3, Baoding Zhu2,4
1Key Laboratory of Exploration Technologies for Oil and Gas Resources and the School of Geophysics and Petroleum Resources, Yangtze University, Wuhan, 430100, China.
Scientific reports
|December 18, 2025
概括
这项研究引入了两个卷积神经网络 (CNN),U-Net和DnCNN,以从通过聚焦离子束扫描电子显微镜 (FIB-SEM) 获得的3D纳米数字岩石图像中去除"窗噪声". 两种CNN都显著提高了图像质量和孔径估计的准确性.
科学领域:
- 地质科学是地球科学.
- 材料科学 材料科学 材料科学
- 图像处理 图像处理
背景情况:
- 聚焦离子束扫描电子显微镜 (FIB-SEM) 对3D纳米数字岩石成像至关重要.
- 在FIB-SEM图像中的"幕后噪声"降低了质量,阻碍了准确的分析.
- 现有的无声化方法不足以消除这种特定的噪音.
研究的目的:
- 开发和评估卷积神经网络 (CNN),用于在FIB-SEM数字岩石图像中拒绝"窗噪声".
- 为了比较U-Net和Denoising卷积神经网络 (DnCNN) 模型的性能.
- 评估剥光对数字岩石孔隙度估计准确性的影响.
主要方法:
- 实施U-Net和DnCNN模型来消除窗噪声.
- 使用FIB-SEM数字岩石图像数据集进行实验评估.
- 使用峰值信号与噪声比率 (PSNR),结构相似度指数测量 (SSIM) 和学习感知图像补丁相似度 (LPIPS) 的定量评估.
主要成果:
- 无论是U-Net还是DnCNN都显著提高了FIB-SEM的图像质量.
- 与DnCNN (PSNR: 27.28dB,SSIM: 0.38,LPIPS: 0.45) 相比,U-Net实现了更高的PSNR (29.10dB),SSIM (0.73) 和LPIPS (0.31) 的结果.
- 无化提高了多孔度估计的准确性,将真多孔度为6.3%的样本的误差从7.9%降至6.1% (U-Net) 和6.0% (DnCNN).
结论:
- U-Net 展示了卓越的无色化性能,有效地保留了图像纹理和细节.
- DnCNN有效地减少了窗噪声,但引入了光滑和模糊,具有较弱的概括性.
- 拟议的基于CNN的化方法为准确的3D纳米数字岩石重建提供了基础.
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