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Image quality metric parameter for holographic modal wavefront sensing.

Anitta Jomy, Aryan Chand, Dinesh N Naik

    Optics Letters
    |May 1, 2026
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    Summary

    We developed a new image quality metric parameter (QMP) for holographic modal wavefront sensing (HMWS). This novel QMP improves accuracy by overcoming limitations of traditional methods, validated by simulations and experiments.

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    Area of Science:

    • Optics and Photonics
    • Image Processing
    • Wavefront Sensing

    Background:

    • Holographic modal wavefront sensing (HMWS) is crucial for optical system characterization.
    • Conventional HMWS methods face limitations including focal point dependence and biased measurements.
    • There is a need for more robust and accurate image quality metrics in HMWS.

    Purpose of the Study:

    • To introduce a novel image quality metric parameter (QMP) for enhanced holographic modal wavefront sensing (HMWS).
    • To address and overcome the limitations of existing QMP approaches in HMWS.
    • To validate the proposed QMP's effectiveness through simulations and experimental data.

    Main Methods:

    • Derivation of the QMP from the spatial intensity distribution co-occurrence matrix.
    • Implementation of the QMP within the holographic modal wavefront sensing framework.
    • Validation using both standard HMWS techniques and convolutional neural network approaches.

    Main Results:

    • The proposed QMP demonstrates significant improvements over conventional metrics.
    • The QMP overcomes limitations related to focal point location, aperture diameter, and biased values.
    • Simulation and experimental results confirm the viability and significance of the novel QMP.

    Conclusions:

    • The novel QMP offers a more robust and accurate method for image quality assessment in HMWS.
    • This advancement has the potential to improve the performance and reliability of optical systems utilizing HMWS.
    • The QMP provides a valuable tool for both standard HMWS and advanced machine learning-based approaches.