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Orthonormalization method in ghost imaging.

Bin Luo, Pengqi Yin, Longfei Yin

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    Summary
    This summary is machine-generated.

    Computational ghost imaging reduces sampling needs using a novel Gram-Schmidt process for pattern orthonormalization. This improves image acquisition efficiency, significantly lowering the required sample count for ghost imaging systems.

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    Area of Science:

    • Optics
    • Computational Imaging
    • Signal Processing

    Background:

    • Ghost imaging typically requires numerous samples for object reconstruction.
    • Existing computational ghost imaging methods use pre-modulated orthogonal patterns to improve efficiency.
    • The rotating ground glass (RGG) scheme traditionally lacks this pre-modulation capability.

    Purpose of the Study:

    • To introduce a post-processing method for enhancing ghost imaging with RGG schemes.
    • To reduce the sampling number requirement and improve imaging quality.
    • To address limitations of existing methods in noisy environments.

    Main Methods:

    • A post-processing Gram-Schmidt orthonormalization method (SGI) was applied to patterns in an RGG scheme.
    • Image quality was evaluated using Contrast-to-Noise Ratio (CNR), Peak Signal to Noise Ratio (PSNR), Correlation Coefficient (CC), and Mean Square Error (MSE).
    • An improved Group SGI method was developed to mitigate error accumulation in noisy systems.

    Main Results:

    • The SGI method significantly reduced the sampling number requirement from thousands to hundreds in ideal conditions.
    • Image quality from SGI degraded with large sampling numbers in noisy systems due to error accumulation.
    • The Group SGI method demonstrated effective reconstruction from experimental data and good performance with large sampling numbers.

    Conclusions:

    • The Gram-Schmidt process offers an efficient post-processing approach for ghost imaging with RGG schemes.
    • The improved Group SGI method overcomes noise-related limitations, enhancing robustness and performance.
    • These methods are compatible with existing reconstruction algorithms, improving overall efficiency.