Journals Articles Conferences News About CLP
Submit a paper Email Alert
Search
Search
Articles
Journals
News
Advanced Search
Top Searches
2D MaterialsTransformation opticsQuantum PhotonicsSmall lasersSemiconductor UV PhotonicsSupersymmetric microring laser arrays

Acta Optica Sinica, Volume. 40, Issue 16, 1611003(2020)

Lens-Free Imaging Method Based on Generative Adversarial Networks

Chao Zhang1,2, Tao Xing1,2, Zizhen Liu1,2, Haokun He1,2, Hua Shen1,2、*, Yinxu Bian1,2, and Rihong Zhu1,2
Author Affiliations
  • 1School of Electronic and Optical Engineering, Nanjing University of Science & Technology, Nanjing, Jiangsu 210094, China;
  • 2Key Laboratory of Advanced Solid Laser, Ministry of Industry and Information Technology, Nanjing, Jiangsu 210094, China
    • show less
    AbstractGet PDF(in Chinese)
    Figures&Tables (10)
    Equations (0)
    References (28)
    Cited By (4)
    Tools
    Paper Information
    Topics
    Figures & Tables(10)
    Lens-free imaging experimental device and light path. (a) Diagram of experimental device; (b) diagram of light path

    Fig. 1. Lens-free imaging experimental device and light path. (a) Diagram of experimental device; (b) diagram of light path

    Download full size
    Flow chart of lens-free imaging processing

    Fig. 2. Flow chart of lens-free imaging processing

    Download full size
    Calculation principle of defocus distance

    Fig. 3. Calculation principle of defocus distance

    Download full size
    Object image reconstructed by back-propagation

    Fig. 4. Object image reconstructed by back-propagation

    Download full size
    Diagram of GAN principle

    Fig. 5. Diagram of GAN principle

    Download full size
    Main components of GAN. (a) GN; (b) DN

    Fig. 6. Main components of GAN. (a) GN; (b) DN

    Download full size
    Image processing results based on GAN. (a) GAN input; (b) partial enlarged image of Fig.7(a); (c) GAN output; (d) partial enlarged image of Fig.7(c)

    Fig. 7. Image processing results based on GAN. (a) GAN input; (b) partial enlarged image of Fig.7(a); (c) GAN output; (d) partial enlarged image of Fig.7(c)

    Download full size
    Apical bud slitting images. (a) Lens-free image; (b) reconstructed object plane image through back-propagation; (c) GAN result; (d) microscope image

    Fig. 8. Apical bud slitting images. (a) Lens-free image; (b) reconstructed object plane image through back-propagation; (c) GAN result; (d) microscope image

    Download full size
    PSNR. (a) Reconstructed object plane image through back-propagation; (b) CNN result; (c) GAN result; (d) microscope image

    Fig. 9. PSNR. (a) Reconstructed object plane image through back-propagation; (b) CNN result; (c) GAN result; (d) microscope image

    Download full size
    Performance comparison between GAN and CNN

    Fig. 10. Performance comparison between GAN and CNN

    Download full size
    Tools

    Get Citation

    Copy Citation Text

    Chao Zhang, Tao Xing, Zizhen Liu, Haokun He, Hua Shen, Yinxu Bian, Rihong Zhu. Lens-Free Imaging Method Based on Generative Adversarial Networks[J]. Acta Optica Sinica, 2020, 40(16): 1611003

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Set citation alerts for article
    Save article for my favorites
    Paper Information

    Category: Imaging Systems

    Received: Jan. 14, 2020

    Accepted: May. 18, 2020

    Published Online: Aug. 7, 2020

    The Author Email: Shen Hua (edward_bayun@163.com)

    DOI:10.3788/AOS202040.1611003

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology