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    This study introduces a novel surrogate model for optical depth to accurately estimate fog density and improve image defogging. The method utilizes a refined polynomial regression model with key fog-relevant features for effective fog removal.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Accurate fog density estimation and effective image defogging are crucial for various applications.
    • Existing methods often struggle with precise fog density assessment and robust fog removal.
    • The optical depth is a key parameter influencing fog's visual impact on images.

    Purpose of the Study:

    • To develop a novel surrogate model for optical depth to improve fog density estimation.
    • To propose an effective method for image defogging based on accurate optical depth modeling.
    • To introduce a new fog-relevant feature derived from the hue, saturation, and value (HSV) color space.

    Main Methods:

    • Investigated various fog-relevant features, including dark-channel, saturation-value, and chroma.
    • Proposed a novel feature based on the hue, saturation, and value (HSV) color space.
    • Employed a surrogate-based method to learn a refined polynomial regression model for optical depth using sensitivity analysis for feature selection.

    Main Results:

    • Developed an accurate surrogate model for optical depth.
    • Achieved effective fog density estimation and image defogging.
    • Demonstrated the method's effectiveness through quantitative and qualitative experiments on synthetic and real-world foggy images.

    Conclusions:

    • The proposed surrogate model for optical depth significantly enhances fog density estimation.
    • The novel feature and refined regression model lead to superior image defogging performance.
    • The method offers a robust solution for processing foggy images in computer vision applications.