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    Area of Science:

    • X-ray imaging
    • Nanoscale science
    • Computational imaging

    Background:

    • High signal-to-noise ratio is critical for ptychographic imaging.
    • Weak diffraction signals in nanoscale experiments are susceptible to parasitic scattering and noise.
    • Correlated noise, outliers, and rare events like cosmic rays degrade image quality.

    Purpose of the Study:

    • To develop a novel iterative algorithm for robust noise characterization and removal in ptychography.
    • To improve the signal-to-noise ratio in nanoscale imaging experiments.
    • To provide a rigorous analysis and convergence proof for the proposed algorithm.

    Main Methods:

    • An iterative algorithm exploiting a direct forward model for parasitic noise.
    • Incorporation of sample smoothness constraints for noise reduction.
    • Formal algorithm description and convergence analysis under mild conditions.

    Main Results:

    • The algorithm effectively characterizes and removes structured and random noise.
    • Demonstrated superior performance compared to state-of-the-art methods in simulations.
    • Validated effectiveness on real data from both soft and hard X-ray beamlines.

    Conclusions:

    • The proposed iterative algorithm significantly enhances image quality in ptychography.
    • It offers a robust solution for noise mitigation in challenging nanoscale imaging scenarios.
    • The method provides a reliable tool for accurate characterization and removal of various noise sources.