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Related Experiment Video

Updated: Dec 21, 2025

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography
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Deep ghost phase imaging.

Koshi Komuro, Takanori Nomura, George Barbastathis

    Applied Optics
    |May 14, 2020
    PubMed
    Summary

    Deep ghost phase imaging (DGPI) uses deep learning for high-quality phase imaging with single-pixel detection. This novel method bypasses complex setups, offering a simpler and effective solution for phase imaging applications.

    Area of Science:

    • Optics and Photonics
    • Computational Imaging
    • Artificial Intelligence

    Background:

    • Single-pixel imaging offers advantages in specific applications but often requires complex setups for phase retrieval.
    • Conventional phase imaging techniques like interferometry and transport-of-intensity methods have limitations in terms of complexity and approximations.

    Purpose of the Study:

    • To propose a novel deep-learning-based method for single-pixel phase imaging.
    • To leverage the benefits of computational ghost imaging for enhanced phase imaging quality.
    • To develop a method that avoids additional measurements and explicit approximations inherent in traditional approaches.

    Main Methods:

    • A deep convolutional neural network (CNN) was trained to reconstruct phase distributions.

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  • The CNN takes defocused intensity measurements as input, reconstructed via single-pixel imaging theory.
  • The method, termed deep ghost phase imaging (DGPI), integrates deep learning with single-pixel detection principles.

    • Main Results:

    • DGPI successfully reconstructs phase information with high signal-to-noise ratio.
    • The method achieves phase imaging quality comparable to advanced techniques without complex optical setups.
    • Numerical simulations and experimental validation confirmed the feasibility and effectiveness of DGPI.

    Conclusions:

    • Deep ghost phase imaging (DGPI) presents a powerful and simplified approach to single-pixel phase imaging.
    • The integration of deep learning with single-pixel imaging overcomes limitations of conventional methods.
    • DGPI demonstrates significant potential for various applications requiring efficient phase retrieval.