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Bayes' theorem-based binary algorithm for fast reference-less calibration of a multimode fiber.

Tianrui Zhao, Liang Deng, Wen Wang

    Optics Express
    |August 19, 2018
    PubMed
    Summary
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    We developed a fast Bayes' theorem algorithm to measure multimode fiber transmission matrices for imaging. This method enables rapid calibration, achieving high-contrast focusing in under a second.

    Area of Science:

    • Optics and Photonics
    • Computational Imaging
    • Biomedical Engineering

    Background:

    • Multimode fibers (MMF) are crucial for transmitting light in imaging systems.
    • Accurate characterization of MMF transmission matrices is essential for high-resolution imaging.
    • Existing calibration methods can be slow and complex.

    Purpose of the Study:

    • To present a novel, high-speed algorithm for measuring the binary transmission matrix of a multimode fiber.
    • To enable reference-less multimode fiber imaging systems.
    • To demonstrate fast light focusing and raster-scanning capabilities.

    Main Methods:

    • Utilized Bayes' theorem and conditional probability to develop a high-speed algorithm.
    • Employed a digital micromirror device (DMD) to control light transmission.

    Related Experiment Videos

  • Defined a threshold for DMD pixel selection to form focused spots.
  • Generated a binary transmission matrix ('0's and '1's).

    • Main Results:

    • Achieved high-enhancement-factor light focusing and raster-scanning at the fiber's distal end.
    • Demonstrated a calibration speed of 0.26 seconds per row for 5000 input-output pairs.
    • Obtained an enhancement factor of 28 for focused spots.
    • The algorithm proved over 10 times faster than the prVBEM algorithm.

    Conclusions:

    • The proposed Bayes' theorem-based binary algorithm offers a significant speed improvement for MMF calibration.
    • This method is effective for achieving tightly focused spots in MMF imaging.
    • The algorithm has broad applicability in focusing light through disordered media, including endoscopic imaging.