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    Ghost imaging quality is improved by addressing errors from limited sampling and imperfect illumination. A new algorithm normalizes illumination coherence, reducing image distortions caused by non-uniform intensity and power fluctuations.

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    Area of Science:

    • Optics and Photonics
    • Image Reconstruction
    • Quantum Imaging

    Background:

    • Ghost imaging systems reconstruct images using correlations between light patterns.
    • Image quality is often degraded by factors like limited sampling and illumination imperfections.
    • Point spread function errors limit the fidelity of reconstructed images.

    Purpose of the Study:

    • To enhance the image reconstruction quality in ghost imaging systems.
    • To address and mitigate errors in the point spread function.
    • To reduce image artifacts caused by imperfect illumination and limited sampling.

    Main Methods:

    • Discussed errors in the point spread function of ghost imaging systems.
    • Proposed a novel algorithm for ghost imaging.
    • The algorithm utilizes normalization with the second-order coherence of the illumination field.

    Main Results:

    • The proposed algorithm effectively reduces errors caused by imperfect illumination.
    • Demonstrated reduction in artifacts from non-uniform intensity distribution.
    • Showed mitigation of errors due to spatially varying coherence profiles and power fluctuations.

    Conclusions:

    • The normalization algorithm offers a significant improvement in ghost imaging reconstruction quality.
    • Effective in reducing various types of illumination-induced errors.
    • Provides a pathway for more robust and accurate ghost imaging applications.