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    This study introduces a new method to improve the binarization of noisy electronic speckle pattern interferometry (ESPI) fringe patterns. The technique enhances image contrast and uses fuzzy C-means clustering for clearer fringe classification.

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    Area of Science:

    • Optical Metrology
    • Image Processing
    • Interferometry

    Background:

    • Electronic Speckle Pattern Interferometry (ESPI) is crucial for measuring deformations.
    • ESPI fringe patterns suffer from inherent speckle noise and low contrast, complicating analysis.
    • Accurate binarization of fringe patterns is essential for quantitative interpretation.

    Purpose of the Study:

    • To develop an effective binarization method for low-quality ESPI fringe patterns.
    • To enhance the contrast and reduce noise in ESPI fringe images.
    • To accurately classify fringe pixels into bright and dark categories.

    Main Methods:

    • Preprocessing using the multiscale retinex (MSR) algorithm for contrast enhancement.
    • Local entropy calculation and filtering using a second-order oriented partial differential equation.
    • Fuzzy C-means (FCM) clustering for classifying fringe pixels.

    Main Results:

    • The proposed method successfully enhances contrast in low-quality ESPI fringe patterns.
    • Filtered local entropy maps facilitate effective fringe segmentation.
    • FCM clustering accurately classifies pixels into bright and dark fringes.
    • Qualitative evaluation on experimental and simulated ESPI patterns demonstrates good binarization performance.

    Conclusions:

    • The combined MSR, local entropy filtering, and FCM clustering method offers a robust solution for ESPI fringe binarization.
    • This approach significantly improves the analysis of challenging ESPI fringe patterns.
    • The method shows reliability and universality for various ESPI applications.