Portable deep learning singlet microscope

被引:10
|
作者
Shen, Hua [1 ,2 ]
Gao, Jinming [1 ]
机构
[1] Nanjing Univ Sci & Technol, Sch Elect & Opt Engn, Nanjing 210094, Peoples R China
[2] Univ Calif Los Angeles, Dept Mat Sci & Engn, Los Angeles, CA 90024 USA
基金
中国国家自然科学基金;
关键词
aspheric lens; biologic imaging; computational imaging; optical design; singlet microscopy; GRADIENT-INDEX; WIDE-FIELD; SYSTEM;
D O I
10.1002/jbio.202000013
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Having the least lenses, the significant feature of the singlet imaging system, helps the development of the portable and cost-effective microscopes. A novel method of monochromatic/color singlet microscopy, which is combined with only one aspheric lens and deep learning computational imaging technology, is proposed in this article. The designed singlet aspheric lens is an approximate linear signal system, which means modulation-transfer-function curves on all field-of-views (5 mm diagonally) are almost coincident with each other. The purpose of the designed linear signal system is to further improve the resolution of our microscope by using deep learning algorithm. As a proof of concept, we designed a singlet microscopy based on our method, which weighs only 400 g. The experimental data and results of the sample USAF-1951 target and bio-sample (the Equisetum-arvense Strobile L.S), prove that the performance of the proposed singlet microscope is competitive to a commercial microscope with the 4X/NA0.1 objective lens. We believe that our idea and method would guide to design more cost-effective and powerful singlet imaging system.
引用
收藏
页数:10
相关论文
共 50 条
  • [21] Deep Learning Based Portable Respiratory Sound Classification System
    Edakkadan, Adithya Sunil
    Srivastava, Abhishek
    2023 IEEE ASIA PACIFIC CONFERENCE ON CIRCUITS AND SYSTEMS, APCCAS, 2024, : 129 - 133
  • [22] Custom Hardware Architectures for Deep Learning on Portable Devices: A Review
    Zaman, Kh Shahriya
    Reaz, Mamun Bin Ibne
    Ali, Sawal Hamid Md
    Bakar, Ahmad Ashrif A.
    Chowdhury, Muhammad Enamul Hoque
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 33 (11) : 6068 - 6088
  • [23] Deep learning virtual Zernike phase contrast imaging for singlet microscopy
    Bian, Yinxu
    Jiang, Yannan
    Deng, Weijie
    Shen, Renbing
    Shen, Hua
    Kuang, Cuifang
    AIP ADVANCES, 2021, 11 (06)
  • [24] Cell Image classification of microscope application with deep learning technology
    Tseng, Kuo-Kun
    Jin, Qi
    Chen, Charles
    Lin, Regina Fang-Ying
    Chen, Boxi
    Zhang, Chunxin
    BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY, 2019, 125 : 228 - 228
  • [25] Deep Learning-Based Autonomous Scanning Electron Microscope
    Jang, Jonggyu
    Lyu, Hyeonsu
    Yang, Hyun Jong
    Oh, Moohyun
    Lee, Junhee
    2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2020, : 2886 - 2893
  • [26] Application of Deep Learning in Segmentation of Cell Image by Optical Microscope
    Jia Ce
    Cao Guang-Fu
    Wang Xiao-Feng
    Zhang Xiang
    PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS, 2022, 49 (02) : 395 - 400
  • [27] A Portable Miniature Microscope for Biomedical Applications
    Song, Yang
    Yang, Xi-bin
    Yan, Bing
    Zhou, Wei
    Wang, Chi
    Xiong, Da-xi
    AOPC 2019: OPTICAL SPECTROSCOPY AND IMAGING, 2019, 11337
  • [28] Portable optical microscope-on-a-chip
    Cui, Xiquan
    Heng, Xin
    Erickson, David
    Psaltis, Demetri
    Yang, Changhuei
    NANOBIOPHOTONICS AND BIOMEDICAL APPLICATIONS III, 2006, 6095
  • [29] Portable scanning electron microscope designs
    Khursheed, A
    JOURNAL OF ELECTRON MICROSCOPY, 1998, 47 (06): : 591 - 602
  • [30] A portable laser photostimulation and imaging microscope
    Nikolenko, Volodymyr
    Peterka, Darcy S.
    Yuste, Rafael
    JOURNAL OF NEURAL ENGINEERING, 2010, 7 (04)