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Large depth-of-field ultra-compact microscope by progressive optimization and deep learning

Engineering and Technology

Large depth-of-field ultra-compact microscope by progressive optimization and deep learning

Y. Zhang, X. Song, et al.

Discover groundbreaking research by Yuanlong Zhang and colleagues on a miniaturized integrated microscope that outperforms commercial models, boasting a compact design perfect for portable diagnostics. Utilizing advanced optics and deep learning, this innovative technology offers tenfold improvement in depth-of-field!

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Playback language: English
Abstract
This paper presents a miniaturized integrated microscope achieving superior optical performance compared to commercial microscopes, while being significantly smaller and lighter. A progressive optimization pipeline combines aspherical lenses, diffractive optical elements, and deep learning for spatially varying deconvolution, resulting in a tenfold improvement in depth-of-field. The microscope's compact design allows for integration into a cell phone, demonstrating its potential for portable diagnostics.
Publisher
Nature Communications
Published On
Jul 11, 2023
Authors
Yuanlong Zhang, Xiaofei Song, Jiachen Xie, Jing Hu, Jiawei Chen, Xiang Li, Haiyu Zhang, Qiqun Zhou, Lekang Yuan, Chui Kong, Yibing Shen, Jiamin Wu, Lu Fang, Qionghai Dai
Tags
integrated microscope
optical performance
miniaturized design
deep learning
portable diagnostics
depth-of-field
aspherical lenses

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