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Measuring Spatially- and Directionally-varying Light Scattering from Biological Material
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IDE: Image Dehazing and Exposure Using an Enhanced Atmospheric Scattering Model.

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    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
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    This study introduces an enhanced atmospheric scattering model (EASM) and an image dehazing technique (IDE) to improve hazy image quality. IDE effectively removes the dim effect without training, offering superior dehazing performance and efficiency.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Remote Sensing

    Background:

    • The atmospheric scattering model (ASM) is widely used for hazy image processing but suffers from a dim effect in recovered images.
    • An intrinsic limitation of ASM leads to reduced brightness and contrast in dehazed images, hindering accurate scene representation.

    Purpose of the Study:

    • To address the dim effect in hazy image restoration by introducing an enhanced atmospheric scattering model (EASM).
    • To develop a novel, efficient, and robust image dehazing technique (IDE) based on the EASM for improved visibility of outdoor scenes.

    Main Methods:

    • An enhanced atmospheric scattering model (EASM) was developed by incorporating a light absorption coefficient parameter.
    • A gray-world-assumption-based image enhancement technique (IDE) was designed utilizing the EASM.
    • IDE employs a global stretch strategy to avoid common image restoration artifacts.

    Main Results:

    • The proposed IDE effectively eliminates the dim effect observed in traditional ASM-based methods.
    • Experimental results demonstrate excellent dehazing performance, superior to state-of-the-art techniques in both quality and efficiency.
    • IDE achieved significant improvements in visibility without requiring scene depth information or training data.

    Conclusions:

    • The developed EASM and IDE provide a robust and efficient solution for hazy image enhancement.
    • IDE offers a fast and reliable method for improving the visual quality of outdoor hazy scenes.
    • The proposed approach overcomes limitations of existing methods, offering practical advantages for real-world applications.