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一种基于深度学习的条纹自我校正方法,用于拼接的显微镜图像.

Shu Wang^1,2,3, Xiaoxiang Liu², Yueying Li¹

¹College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China.

Nature communications

|September 5, 2023

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此摘要是机器生成的。

显微镜图像中的条纹文物降低了质量. 一种新的深度学习方法,SSCOR,使用自我校正来删除这些条纹和其他文物,改进图像分析.

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:

合光显微镜图像通常含有条纹文物.
这些文物会对图像质量和定量分析产生负面影响.

研究的目的:

引入一种基于深度学习的方法,SSCOR,用于纠正显微镜图像中的条纹文物.
开发一种方法,可以在没有人工干预的情况下自适应地纠正各种文物.

主要方法:

提出了一个近距离抽样方案和对抗互惠自训练范式.
SSCOR使用无条纹图像补丁来纠正相邻的包含文物补丁.
该方法在不需要物理参数估计或手动注释的情况下运行.

主要成果:

SSCOR有效地纠正不均,斜和网格条纹.
该方法还可以删除扫描,泡和失焦的文物.
在各种成像条件和模式中实现了最先进的性能.

结论:

对于显微镜图像恢复,SSCOR提供了一个智能,无先验的解决方案.
这种方法提高了图像质量,用于更精确的生物医学应用.

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为研究人员和显微镜制造商提供了宝贵的工具.