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    Area of Science:

    • Image Processing
    • Computational Physics
    • Applied Mathematics

    Background:

    • Phase unwrapping algorithms are essential for recovering smooth phases from wrapped data.
    • Existing noniterative algorithms often provide insufficient accuracy due to global approaches.
    • Accurate smooth phase estimations are vital for robust iterative reconstruction techniques.

    Purpose of the Study:

    • To develop an improved method for generating smooth phase initial guesses.
    • To enhance the accuracy of phase reconstruction in applications requiring precise phase recovery.
    • To leverage local polynomial modeling for more precise phase derivative estimation.

    Main Methods:

    • Utilizing local polynomial approaches up to the third order.
    • Applying least-squares fitting to the partial derivatives of the phase.
    • Estimating phase derivatives from the wrapped phase operator.
    • Testing the method with both synthetic and real-world wrapped phase data.

    Main Results:

    • The proposed method offers a more accurate smooth phase estimation compared to traditional global methods.
    • Local polynomial fitting effectively approximates phase derivatives, improving initial guess quality.
    • Demonstrated applicability on both simulated and experimental wrapped phase data.

    Conclusions:

    • The developed local fitting technique provides a valuable tool for enhancing phase unwrapping accuracy.
    • This method offers a significant improvement for iterative phase reconstruction algorithms.
    • The approach is robust and applicable to diverse real-world phase recovery challenges.