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    Area of Science:

    • Optics and optical measurement
    • Interferometry
    • Machine learning applications

    Background:

    • Newton's rings are optical fringes with quadratic phase, crucial for spherical measurements.
    • Analyzing Newton's ring patterns is essential but challenging.
    • Existing methods for fringe analysis have limitations.

    Purpose of the Study:

    • To present a novel deep-learning-based method for Newton's ring pattern analysis.
    • To accurately estimate parameters of Newton's ring patterns.
    • To improve the efficiency and robustness of fringe analysis.

    Main Methods:

    • Development of a deep learning model for fringe parameter estimation.
    • Application of the model to analyze Newton's ring patterns.
    • Experimental validation of the proposed method.

    Main Results:

    • The deep learning method demonstrates excellent accuracy.
    • The method exhibits robustness against noise.
    • High demodulation efficiency was achieved in experimental results.

    Conclusions:

    • The deep learning approach offers a viable alternative for Newton's ring analysis.
    • This method provides valuable insights for fringe analysis and interferometry.
    • It advances the field of optical interferometry-based measurements.