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Dual-band transmittance defogging model.

Jin Duan, Peiren Guo, Suxin Mo

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    Summary

    This study introduces a dual-band defogging model for clearer images in dense fog. It improves upon existing methods by calculating transmittance for each band separately, enhancing details in hazy areas.

    Area of Science:

    • Computer Vision
    • Image Processing
    • Remote Sensing

    Background:

    • Shortwave infrared (SWIR) and visible light fusion is effective for image defogging.
    • Existing methods struggle with dense fog and rely on flawed atmospheric scattering models with a constant visible light transmittance assumption.

    Purpose of the Study:

    • To propose a novel dual-band defogging model that overcomes limitations of current fusion techniques.
    • To improve image defogging performance, especially in dense fog conditions, by addressing inaccurate transmittance assumptions.

    Main Methods:

    • A dual-band defogging model is proposed, calculating fusion weights using gradient and saturation.
    • Transmittance is computed separately for each band and combined with fusion weights for an overall weighted transmittance.

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  • The model's performance is evaluated against single-band transmittance models and SWIR fusion algorithms.

    • Main Results:

    • The proposed dual-band model effectively enhances details in hazy regions and improves foreground processing.
    • Subjective visual assessments and objective no-reference image quality assessment (NR-IQA) confirm the model's superiority.
    • The new model outperforms three single-band transmittance models and eight SWIR fusion algorithms.

    Conclusions:

    • The proposed dual-band defogging model offers a significant advancement over existing image defogging techniques.
    • This approach provides better detail preservation and foreground clarity in challenging foggy conditions.
    • The model's effectiveness is validated through comprehensive comparative analysis.