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    A novel binarization method using local entropy and fuzzy c-means (FCM) clustering effectively processes Electronic Speckle Pattern Interferometry (ESPI) fringe patterns. This approach eliminates noise filtering, directly utilizing speckle noise for accurate phase extraction in dynamic measurements.

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    Area of Science:

    • Optical Metrology
    • Image Processing
    • Interferometry

    Background:

    • The fringe skeleton method is crucial for phase extraction in dynamic measurements using Electronic Speckle Pattern Interferometry (ESPI).
    • Traditional binarization techniques for ESPI patterns often require filtering to mitigate speckle noise, adding complexity.
    • Speckle noise in ESPI patterns presents a significant challenge for accurate binarization and subsequent analysis.

    Purpose of the Study:

    • To introduce a new binarization method for ESPI fringe patterns that bypasses the need for noise filtering.
    • To leverage local entropy and fuzzy c-means (FCM) clustering for robust fringe pattern segmentation.
    • To demonstrate the efficacy of the proposed method on both simulated and real-world ESPI data.

    Main Methods:

    • A novel binarization approach utilizing local entropy computed for each pixel.
    • Application of the fuzzy c-means (FCM) clustering algorithm to segment pixels into fringe categories based on local entropy.
    • Direct processing of ESPI fringe patterns without prior noise filtering, incorporating speckle noise information.

    Main Results:

    • The proposed method successfully binarizes ESPI fringe patterns by clustering pixels based on local entropy.
    • Speckle noise is effectively handled and utilized within the segmentation process, eliminating the need for preprocessing filters.
    • Accurate binarization results were achieved for both computer-simulated and experimentally acquired ESPI fringe patterns.

    Conclusions:

    • The local entropy and FCM-based binarization method provides an effective alternative to traditional filtering-dependent approaches for ESPI analysis.
    • This technique simplifies the fringe analysis workflow by integrating noise handling into the binarization step.
    • The method demonstrates its capability to yield high-quality skeletonization results essential for precise phase extraction in dynamic measurements.