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Representing Noisy Image Without Denoising.

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    This study introduces Fractional-order Moments in Radon space (FMR) for robust pattern recognition in noisy images. FMR offers improved noise robustness, rotation invariance, and time-frequency analysis without prior denoising.

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    Area of Science:

    • Artificial Intelligence
    • Image Processing
    • Pattern Recognition

    Background:

    • Traditional AI methods struggle with pattern recognition in noisy images.
    • Current data-driven approaches like data augmentation and image denoising are often inefficient and unstable.
    • There is a need for more direct and robust methods for extracting features from noisy images.

    Purpose of the Study:

    • To develop a novel, non-learning paradigm for deriving robust representations directly from noisy images.
    • To introduce Fractional-order Moments in Radon space (FMR) as a noise-robust feature descriptor.
    • To enhance pattern recognition capabilities in challenging image conditions.

    Main Methods:

    • Exploration of a non-learning paradigm for direct robust representation extraction.
    • Design of Fractional-order Moments in Radon space (FMR) incorporating orthogonality and rotation invariance.
    • Formal discussion of implicit and explicit methods for FMR construction.
    • Utilizing fractional-order parameters for enhanced time-frequency analysis.

    Main Results:

    • FMR demonstrates superior noise robustness compared to traditional methods.
    • The proposed method achieves rotation invariance.
    • FMR provides unique time-frequency analysis capabilities.
    • Extensive simulations and applications validate the effectiveness of FMR.

    Conclusions:

    • Fractional-order Moments in Radon space (FMR) offer a unique and effective approach to pattern recognition from noisy images.
    • The FMR method surpasses limitations of existing data-driven techniques by avoiding pre-processing steps.
    • The developed technique provides significant advantages in noise robustness, rotation invariance, and time-frequency discriminability for practical applications.