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Fast structured illumination microscopy via deep learning
Photonics Research ( IF 7.6 ) Pub Date : 2020-07-21 , DOI: 10.1364/prj.396122
Chang Ling , Chonglei Zhang , Mingqun Wang , Fanfei Meng , Luping Du , Xiaocong Yuan

This study shows that convolutional neural networks (CNNs) can be used to improve the performance of structured illumination microscopy to enable it to reconstruct a super-resolution image using three instead of nine raw frames, which is the standard number of frames required to this end. Owing to the isotropy of the fluorescence group, the correlation between the high-frequency information in each direction of the spectrum is obtained by training the CNNs. A high-precision super-resolution image can thus be reconstructed using accurate data from three image frames in one direction. This allows for gentler super-resolution imaging at higher speeds and weakens phototoxicity in the imaging process.

中文翻译:

通过深度学习快速结构化照明显微镜

这项研究表明,卷积神经网络 (CNN) 可用于提高结构化照明显微镜的性能,使其能够使用三个而不是九个原始帧重建超分辨率图像,这是为此所需的标准帧数. 由于荧光组的各向同性,通过训练CNNs获得光谱各个方向的高频信息之间的相关性。因此,可以使用来自一个方向的三个图像帧的准确数据重建高精度超分辨率图像。这允许以更高的速度进行更温和的超分辨率成像,并削弱成像过程中的光毒性。
更新日期:2020-07-21
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