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    This study introduces a novel defogging method that accounts for varying extinction coefficients across spectral channels, significantly improving image restoration and target recognition in foggy conditions.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Remote Sensing

    Background:

    • Current defogging models often assume constant attenuation coefficients, leading to spectral distortion and information bias.
    • This limitation hinders accurate restoration and analysis of multispectral foggy images.

    Purpose of the Study:

    • To develop an advanced defogging method that considers spectral variations in extinction coefficients.
    • To enhance the restoration of multispectral foggy images and improve target recognition.

    Main Methods:

    • Proposing a defogging approach that differentiates extinction coefficients for multispectral channels.
    • Reconstructing spatially distributed transmission maps for each spectral channel.
    • Characterizing fog using multispectral relative extinction coefficients.

    Main Results:

    • The proposed method demonstrates superior performance in restoring lost details and compensating for degraded spectral information.
    • Experimental results show enhanced recognition of targets obscured by fog compared to existing technologies.
    • The method effectively reconstructs multispectral information by leveraging fog's intrinsic properties.

    Conclusions:

    • The novel defogging method effectively addresses spectral distortion by considering channel-specific extinction coefficients.
    • This approach offers significant advantages for multispectral image restoration and analysis in foggy environments.
    • The characterization of fog via multispectral relative extinction coefficients provides a foundation for future research.