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Lower-upper-threshold correlation for underwater range-gated imaging self-adaptive enhancement.

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    A new method enhances underwater images by adaptively adjusting thresholds to improve brightness and contrast without losing details. This technique benefits underwater range-gated imaging (URGI) for clearer human observation.

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    Area of Science:

    • Optics and Imaging
    • Computer Vision
    • Image Processing

    Background:

    • Underwater range-gated imaging (URGI) requires enhanced low-brightness, low-contrast images for effective human observation.
    • Conventional histogram equalization methods often lead to over-enhancement and loss of image details.

    Purpose of the Study:

    • To propose a novel self-adaptive enhancement method for URGI.
    • To address the over-enhancement issue in traditional methods and preserve image details.

    Main Methods:

    • A lower-upper threshold correlation method based on double-plateau histogram equalization is introduced.
    • The method adaptively determines upper and lower thresholds by real-time local maximum searching and filtered histogram analysis.
    • This approach constrains image backgrounds while enhancing details.

    Main Results:

    • Experimental evaluations using PSNR, variance, contrast, and human visual properties demonstrated the method's effectiveness.
    • The proposed method adaptively selects optimal thresholds across various conditions.
    • Objective quality metrics confirmed improved global and region-of-interest image quality.

    Conclusions:

    • The developed method provides effective self-adaptive image enhancement for URGI.
    • It successfully balances detail enhancement with over-enhancement compression.
    • The technique significantly contributes to improved visual clarity in underwater imaging applications.