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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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Variational model for simultaneously image denoising and contrast enhancement.

Wei Wang, Caixia Zhang, Michael K Ng

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    |July 17, 2020
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    Summary

    This study introduces a new variational model for simultaneous image denoising and contrast enhancement. The method effectively removes noise while improving image contrast, outperforming existing techniques.

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    Area of Science:

    • Image Processing
    • Computer Vision
    • Applied Mathematics

    Background:

    • Image noise significantly degrades contrast enhancement performance.
    • Existing methods often struggle to balance denoising and contrast improvement.

    Purpose of the Study:

    • To develop a unified variational model for simultaneous image denoising and contrast enhancement.
    • To address the limitations of separate denoising and enhancement techniques.

    Main Methods:

    • A novel variational model incorporating a histogram equalization term for contrast enhancement.
    • Inclusion of a total variation term for effective noise removal.
    • A fidelity term to preserve image structure and texture.

    Main Results:

    • The proposed model achieves simultaneous noise reduction and contrast enhancement.
    • Experimental results demonstrate superior performance compared to existing methods.
    • Quantitative evaluation using measures like structural similarity index and average local contrast.

    Conclusions:

    • The developed variational model offers an effective solution for enhancing noisy images.
    • The simultaneous approach provides a robust method for image quality improvement.
    • The model's mathematical foundation ensures the existence of a minimizer and algorithm convergence.