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    This study introduces a new unsupervised method for single-shot phase retrieval, improving image detail reconstruction. The algorithm enhances accuracy for complex objects, even with limited measurements and noise.

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    Area of Science:

    • Coherent imaging
    • Computational imaging
    • Image reconstruction

    Background:

    • Phase retrieval is essential for reconstructing amplitude and phase in coherent imaging.
    • Single-shot phase retrieval faces challenges due to limited measurements, hindering fine detail resolution.

    Purpose of the Study:

    • To develop an unsupervised, noise-robust single-shot phase retrieval algorithm.
    • To enhance the reconstruction of fine details and texture in complex-valued objects.

    Main Methods:

    • Integration of alternating direction optimization.
    • Application of weighted complex structure tensor total variation regularization.
    • Balancing data fitting with texture preservation.

    Main Results:

    • The proposed algorithm demonstrates noise robustness.
    • Successful reconstruction of complex amplitude for texture-rich objects.
    • Enhanced fidelity and fine detail resolution in simulations and experiments.

    Conclusions:

    • The developed method effectively addresses limitations in single-shot phase retrieval.
    • It offers a robust solution for reconstructing complex-valued objects with high fidelity.
    • The approach shows significant potential for advanced imaging applications.