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Super-resolution Fluorescence Microscopy01:37

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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
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相关实验视频

Updated: Jun 11, 2025

Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization
10:28

Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization

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高分辨率无透镜全息显微镜,使用物理意识深度网络.

Ashwini S Galande1, Vikas Thapa1, Aswathy Vijay1

  • 1Indian Institute of Technology Hyderabad, Department of Biomedical Engineering, Medical Optics and Sensors Laboratory, Hyderabad, Telangana, India.

Journal of biomedical optics
|October 9, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了HDPhysNet,这是一种用于无透镜数字直线全息显微镜 (LDIHM) 的混合深度学习模型. HDPhysNet 增强了单个全息图的阶段恢复,提高了生物样本的分辨率和性能,用于护理点的应用.

关键词:
宫细胞的细胞.高分辨率的高分辨率显示器没有镜头的全息画.意识到物理的神经网络.

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相关实验视频

Last Updated: Jun 11, 2025

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科学领域:

  • 定量的相位成像成像技术
  • 计算显微镜的计算显微镜
  • 深度学习用于成像.

背景情况:

  • 无镜头数字直线全息显微镜 (LDIHM) 是一种用于定量相位成像的新兴技术.
  • 现有的LDIHM深度学习方法需要大量的训练数据或缺乏复杂的生物样本的稳定性.
  • 具有物理意识的深度网络可以在没有事先培训的情况下改进重建,但在数据保真方面存在困难.

研究的目的:

  • 开发混合深度学习框架,将训练有素和未训练有素的模型结合起来,用于LDIHM的高分辨率阶段恢复.
  • 克服单独训练有素和物理意识的未训练有素深度网络的局限性.
  • 为了从单个低分辨率全息图中实现高准确度的相位重建.

主要方法:

  • 提出了一个混合深度框架 (HDPhysNet),集成预训练的高清生成对抗网络 (HDGAN) 和物理意识的未经训练的深度网络.
  • 采用了plug-and-play方法,其中HDGAN生成高分辨率阶段,然后调整物理意识网络的重建.
  • 在损失函数中整合全息物理,以实现强大的相位恢复.

主要成果:

  • 与纯训练或未训练的深度网络相比,HDPhysNet表现更好,具有更高的结构相似度指数 (SSIM) 和相位信号噪声比 (SNR).
  • 在实验生物细胞 (子宫和红细胞) 上取得了相位SNR (8.2-9.8dB) 的显著改善.
  • 显示了对成像参数 (如传播距离和波长) 的干扰的增强强度.

结论:

  • HDPhysNet有效地结合了训练有素和未训练有素的深度学习模型的优势,以在LDIHM中进行高级阶段恢复.
  • 拟议的方法提供了更好的准确性和稳定性,特别是在复杂的生物成像中.
  • 与HDPhysNet集成的LDIHM提供了一个有前途的,便携式显微镜解决方案,用于照顾点细胞学.