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物理嵌入式神经状态空间模块用于无单射镜片的芯片内显微镜

Pengcheng Jia, Sida Gao, Ziyang Li

|October 15, 2025

概括

我们开发了一种新的AI框架,用于无镜头芯片显微镜 (LFOCM),它利用衍射物理学重建高质量的图像. 这种方法可以显著降低噪音,并改善高级成像应用的图像细节.

科学领域:

光学和光子学在光学和光子学.
生物医学成像技术生物医学成像技术
显微镜中的人工智能

背景情况:

没有透镜的芯片显微镜 (LFOCM) 提供了高通量成像,没有庞大的光学.
传统的LFOCM重建方法会放大噪声,因为它们依赖于基于物理的先验.
需要改进的重建技术来提高图像保真度和减少文物.

研究的目的:

开发一种新的嵌入物理的神经状态空间框架,用于一次性LFOCM重建.
解决噪声放大和人工制造传统方法的局限性.
为了在LFOCM中实现高准确度的定量振幅和相恢复.

主要方法:

开发了一个嵌入物理的神经状态空间框架,结合了衍射物理.
使用了双分支的神经架构,结合了本地和全球建模.
采用无监督学习方法进行图像重建.

主要成果:

实现了最先进的噪声强度和重建保真度.
证明了同时抑制文物和保存分辨率.
成功地恢复了高保真度的定量振幅和相位分布.

结论:

拟议的物理限制框架显著提高了LFOCM图像质量.
这种方法克服了传统重建管道固有的噪声放大问题.
该方法使先进的定量成像能够提高准确性和细节性.

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