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Updated: Sep 11, 2025

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Robust lensless computational imaging via nonlinear weighting approach.

Jun Lan, Cheng Xu, Hui Pang

    Applied Optics
    |August 12, 2025
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    Summary

    This study introduces a novel nonlinear weighting strategy for multi-image phase retrieval, improving sample reconstruction accuracy by down-weighting noisy or erroneous diffraction images. This method enhances image quality and robustness in phase retrieval algorithms.

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    Area of Science:

    • Diffraction imaging
    • Computational imaging
    • Materials science

    Background:

    • Multi-image phase retrieval commonly assumes equal image accuracy, neglecting experimental noise and errors.
    • Inconsistent noise and systematic errors significantly degrade diffraction image quality and reconstruction fidelity.

    Purpose of the Study:

    • To introduce a nonlinear weighting strategy into parallel mode phase retrieval algorithms.
    • To mitigate the impact of noisy and erroneous diffraction images on sample reconstruction.
    • To enhance the accuracy, clarity, and resolution of reconstructed images.

    Main Methods:

    • A nonlinear weighting strategy is developed for parallel mode phase retrieval.
    • Weighting factors are determined by the similarity between measured and calculated amplitudes.
    • Planes with lower similarity (higher error) receive reduced weight during wavefront updating.

    Main Results:

    • The proposed weighting strategy effectively reduces the influence of measurement planes with large errors.
    • Numerical simulations confirm excellent robustness against noise and systematic errors.
    • Experimental reconstruction of three different samples demonstrated high accuracy, clarity, and resolution.

    Conclusions:

    • The developed nonlinear weighting strategy significantly improves multi-image phase retrieval.
    • This approach enhances the quality of reconstructed images in the presence of experimental imperfections.
    • The method offers a robust solution for accurate phase recovery in diffraction imaging.