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Efficient single-pixel imaging based on a compact fiber laser array and untrained neural network.

Wenchang Lai1, Guozhong Lei1, Qi Meng1

  • 1College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha, 410073, China.

Frontiers of Optoelectronics
|April 7, 2024
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Summary

This study introduces an efficient single-pixel imaging (SPI) method using a fiber laser array and deep learning. The technique achieves high-quality imaging with minimal data, promising for remote sensing applications.

Keywords:
Deep learningFiber laser arrayRemote sensingSingle-pixel imaging

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Area of Science:

  • Optics and Photonics
  • Computational Imaging
  • Machine Learning

Background:

  • Single-pixel imaging (SPI) traditionally requires numerous measurements for image reconstruction.
  • Existing SPI methods face limitations in speed and resolution.
  • Fiber laser arrays offer potential for high power and rapid modulation in optical systems.

Purpose of the Study:

  • To develop an efficient single-pixel imaging (SPI) scheme.
  • To leverage a phase-controlled fiber laser array for illumination.
  • To utilize an untrained deep neural network for image reconstruction.

Main Methods:

  • A compact hexagonal fiber laser array was coherently combined to generate light fields.
  • High-speed electro-optic modulators enabled rapid speckle projection.
  • An untrained deep neural network was employed for image reconstruction.

Main Results:

  • High-quality SPI was achieved with a low sampling ratio of 1.6%.
  • Simulations and experiments validated the proposed method's feasibility.
  • The system demonstrated effective image reconstruction from limited measurements.

Conclusions:

  • The proposed SPI scheme using a fiber laser array is efficient and effective.
  • The integration of deep learning enhances image reconstruction quality.
  • This method shows significant potential for remote sensing and other applications requiring high-power, rapid modulation imaging.