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

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 () and Qionghai Dai ()
Additional contact information
Yuanlong Zhang: Tsinghua University
Xiaofei Song: Tsinghua University
Jiachen Xie: Tsinghua University
Jing Hu: Zhejiang University
Jiawei Chen: OPPO Research Institute
Xiang Li: OPPO Research Institute
Haiyu Zhang: OPPO Research Institute
Qiqun Zhou: OPPO Research Institute
Lekang Yuan: Tsinghua University
Chui Kong: Fudan University
Yibing Shen: Zhejiang University
Jiamin Wu: Tsinghua University
Lu Fang: Tsinghua University
Qionghai Dai: Tsinghua University

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: Abstract The optical microscope is customarily an instrument of substantial size and expense but limited performance. Here we report an integrated microscope that achieves optical performance beyond a commercial microscope with a 5×, NA 0.1 objective but only at 0.15 cm3 and 0.5 g, whose size is five orders of magnitude smaller than that of a conventional microscope. To achieve this, a progressive optimization pipeline is proposed which systematically optimizes both aspherical lenses and diffractive optical elements with over 30 times memory reduction compared to the end-to-end optimization. By designing a simulation-supervision deep neural network for spatially varying deconvolution during optical design, we accomplish over 10 times improvement in the depth-of-field compared to traditional microscopes with great generalization in a wide variety of samples. To show the unique advantages, the integrated microscope is equipped in a cell phone without any accessories for the application of portable diagnostics. We believe our method provides a new framework for the design of miniaturized high-performance imaging systems by integrating aspherical optics, computational optics, and deep learning.

Date: 2023
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DOI: 10.1038/s41467-023-39860-0

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