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    A novel bitonic filter preserves image details and reduces noise effectively across various signal types. This adaptable filter outperforms traditional methods, offering practical applicability without complex parameter tuning.

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    Area of Science:

    • Image processing
    • Signal processing
    • Computer vision

    Background:

    • Traditional filters like Gaussian and median have limitations in preserving details while reducing noise.
    • Existing advanced filters often require parameter tuning or prior knowledge of noise levels.

    Purpose of the Study:

    • Introduce a new bitonic filter with superior edge and detail preservation.
    • Evaluate the filter's noise reduction capabilities and applicability to diverse signal and noise types.
    • Compare the bitonic filter's performance against established and advanced filtering techniques.

    Main Methods:

    • The bitonic filter is based on a definition of signal as bitonic (one local maxima or minima).
    • It combines non-linear morphological and linear operators, adapting locally to signal and noise levels.
    • Performance is evaluated on various noisy images and compared to Gaussian, median, image-guided, anisotropic diffusion, non-local means, grain, and leveling/rank filters.

    Main Results:

    • The bitonic filter demonstrates better edge and detail preservation than median filters and comparable noise reduction to Gaussian filters.
    • It outperforms most compared filters in signal-to-noise ratio, except for non-local means and sometimes anisotropic diffusion.
    • The filter provides good visual results, effectively handling varying noise and feature levels without artifactual noise.

    Conclusions:

    • The bitonic filter is a significant improvement over Gaussian and median filters, offering practical advantages.
    • Its adaptability, lack of data-sensitive parameters, and general applicability make it suitable for diverse image processing tasks.
    • The filter is stable, reasonably fast, and does not require prior noise level knowledge or optimization.