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Large depth-of-field ultra-compact microscope by progressive optimization and deep learning
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 cm(3) and 0.5 g, whose size is five order...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10336131/ https://www.ncbi.nlm.nih.gov/pubmed/37433856 http://dx.doi.org/10.1038/s41467-023-39860-0 |
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author | Zhang, Yuanlong Song, Xiaofei Xie, Jiachen Hu, Jing Chen, Jiawei Li, Xiang Zhang, Haiyu Zhou, Qiqun Yuan, Lekang Kong, Chui Shen, Yibing Wu, Jiamin Fang, Lu Dai, Qionghai |
author_facet | Zhang, Yuanlong Song, Xiaofei Xie, Jiachen Hu, Jing Chen, Jiawei Li, Xiang Zhang, Haiyu Zhou, Qiqun Yuan, Lekang Kong, Chui Shen, Yibing Wu, Jiamin Fang, Lu Dai, Qionghai |
author_sort | Zhang, Yuanlong |
collection | PubMed |
description | 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 cm(3) 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. |
format | Online Article Text |
id | pubmed-10336131 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-103361312023-07-13 Large depth-of-field ultra-compact microscope by progressive optimization and deep learning Zhang, Yuanlong Song, Xiaofei Xie, Jiachen Hu, Jing Chen, Jiawei Li, Xiang Zhang, Haiyu Zhou, Qiqun Yuan, Lekang Kong, Chui Shen, Yibing Wu, Jiamin Fang, Lu Dai, Qionghai Nat Commun Article 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 cm(3) 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. Nature Publishing Group UK 2023-07-11 /pmc/articles/PMC10336131/ /pubmed/37433856 http://dx.doi.org/10.1038/s41467-023-39860-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zhang, Yuanlong Song, Xiaofei Xie, Jiachen Hu, Jing Chen, Jiawei Li, Xiang Zhang, Haiyu Zhou, Qiqun Yuan, Lekang Kong, Chui Shen, Yibing Wu, Jiamin Fang, Lu Dai, Qionghai Large depth-of-field ultra-compact microscope by progressive optimization and deep learning |
title | Large depth-of-field ultra-compact microscope by progressive optimization and deep learning |
title_full | Large depth-of-field ultra-compact microscope by progressive optimization and deep learning |
title_fullStr | Large depth-of-field ultra-compact microscope by progressive optimization and deep learning |
title_full_unstemmed | Large depth-of-field ultra-compact microscope by progressive optimization and deep learning |
title_short | Large depth-of-field ultra-compact microscope by progressive optimization and deep learning |
title_sort | large depth-of-field ultra-compact microscope by progressive optimization and deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10336131/ https://www.ncbi.nlm.nih.gov/pubmed/37433856 http://dx.doi.org/10.1038/s41467-023-39860-0 |
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