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    Area of Science:

    • Signal Processing
    • Optical Metrology
    • Image Analysis

    Background:

    • Empirical Mode Decomposition (EMD) is a signal processing technique used for analyzing complex data.
    • Bidimensional Empirical Mode Decomposition (BEMD) extends EMD to 2D data but faces limitations in envelope interpolation.
    • Interferometric fringe patterns contain valuable information but are often corrupted by noise and background.

    Purpose of the Study:

    • To propose a novel 2D generalization of the midpoint-based empirical mode decomposition algorithm (MBEMD).
    • To enhance the analysis of interferometric fringe patterns by improving component separation.
    • To provide a more robust and efficient method for phase demodulation in interferometry.

    Main Methods:

    • Developed a 2D MBEMD algorithm that directly calculates the mean envelope using midpoints between extrema, avoiding interpolation.
    • Applied the MBEMD algorithm to interferometric fringe pattern analysis.
    • Demonstrated the algorithm's ability to separate oscillatory patterns from background and noise.

    Main Results:

    • The proposed MBEMD algorithm exhibits improved spectral selectivity compared to regular BEMD.
    • MBEMD offers better time performance than the standard BEMD.
    • The algorithm effectively isolates the desired oscillatory fringe component from other signal elements.

    Conclusions:

    • MBEMD provides an effective and adaptive method for analyzing various types of interferometric fringe patterns, including those from digital speckle pattern interferometry.
    • This technique enhances phase demodulation accuracy by reducing errors associated with noise and background.
    • The algorithm's flexibility eliminates the need for special tuning for different fringe pattern types.